Oxidation phosphorylation evaluation method and system combined with blood molecular marker

Abstract

The embodiment of the invention provides an oxidative phosphorylation evaluation method and system combined with a blood molecular marker, and belongs to the technical field of biological information. The method comprises the following steps: obtaining target concentration data corresponding to a blood molecular marker of a target object and target clinical data corresponding to clinical indexes of the target object; determining a target association relationship between the blood molecular marker and the clinical index; performing data combination on the target concentration data and the target clinical data according to the target association relationship to obtain target data corresponding to the target object; and evaluating the oxidation phosphorylation degree corresponding to the target object by using the target data according to the degree analysis model to obtain a target evaluation result corresponding to the target object. The problem that in the related technology, when the oxidative phosphorylation degree is evaluated through blood molecular markers and clinical indexes, only the blood molecular markers and the clinical indexes are directly fused, and the relevance between the blood molecular markers and the clinical indexes is not considered, so that the reliability of evaluating the oxidative phosphorylation degree is low is solved.

Description

Technical Field

[0001] The present invention relates to the field of bioinformatics, and in particular to an oxidative phosphorylation evaluation method and system combined with blood molecular markers. Background Art

[0002] Oxidative phosphorylation is a metabolic pathway of cells. This process occurs on the inner membrane of mitochondria of eukaryotes or the cell membrane of prokaryotes, using enzymes therein and the energy released by the oxidation of various nutrients to synthesize adenosine triphosphate. In theory, tumor cells have a higher level of glycolysis than normal cells, suggesting that the level of oxidative phosphorylation may be low in tumor cells. However, recent studies in the field of hematological tumors have shown that tumor growth requires a higher level of glycolysis, and the level of oxidative phosphorylation may also be reduced. Therefore, it is of certain practical significance to combine blood molecular markers with clinical indicators to qualitatively and quantitatively evaluate the degree of oxidative phosphorylation. However, in the related art, when using blood molecular markers and clinical indicators to evaluate the degree of oxidative phosphorylation, the blood molecular markers and clinical indicators are only directly merged without considering the correlation between them, resulting in the problem of low reliability in evaluating the degree of oxidative phosphorylation.

[0003] Therefore, it is urgent to design a technical solution to solve at least one of the above technical problems. Summary of the invention

[0004] The main purpose of the embodiments of the present invention is to provide a method and system for evaluating oxidative phosphorylation in combination with blood molecular markers, aiming to solve the problem that when the related technology uses blood molecular markers and clinical indicators to evaluate the degree of oxidative phosphorylation, the blood molecular markers and clinical indicators are simply directly integrated without considering the correlation between them, resulting in low reliability in evaluating the degree of oxidative phosphorylation.

[0005] In a first aspect, an embodiment of the present invention provides a method for evaluating oxidative phosphorylation in combination with blood molecular markers, comprising:

[0006] Obtaining target concentration data corresponding to the blood molecular markers of the target subject and target clinical data corresponding to the clinical indicators of the target subject;

[0007] Determining the target correlation relationship between the blood molecular marker and the clinical indicator;

[0008] Combining the target concentration data and the target clinical data according to the target association relationship to obtain target data corresponding to the target object;

[0009] The target data is used to evaluate the degree of oxidative phosphorylation corresponding to the target object according to the degree analysis model to obtain a target evaluation result corresponding to the target object.

[0010] In a second aspect, an embodiment of the present invention provides an oxidative phosphorylation evaluation system combined with blood molecular markers, comprising:

[0011] A data acquisition module, used to obtain target concentration data corresponding to the blood molecular markers of the target object and target clinical data corresponding to the clinical indicators of the target object;

[0012] A relationship determination module, used to determine the target association relationship between the blood molecular marker and the clinical indicator;

[0013] A data combination module, used for combining the target concentration data and the target clinical data according to the target association relationship to obtain target data corresponding to the target object;

[0014] The data evaluation module is used to evaluate the oxidative phosphorylation degree corresponding to the target object using the target data according to the degree analysis model to obtain a target evaluation result corresponding to the target object.

[0015] In a third aspect, an embodiment of the present invention further provides a terminal device, comprising a processor, a memory, a computer program stored in the memory and executable by the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein when the computer program is executed by the processor, the steps of any one of the methods for evaluating oxidative phosphorylation combined with blood molecular markers provided in the specification of the present invention are implemented.

[0016] In a fourth aspect, an embodiment of the present invention further provides a storage medium for computer-readable storage, characterized in that the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any method for evaluating oxidative phosphorylation combined with blood molecular markers as provided in the specification of the present invention.

[0017] The embodiment of the present invention provides an oxidative phosphorylation evaluation method and system combined with a blood molecular marker, the method comprising: obtaining target concentration data corresponding to the blood molecular marker of the target object and target clinical data corresponding to the clinical index of the target object, and then determining the target association relationship between the blood molecular marker and the clinical index according to expert experience or historical experience. After determining the target association relationship, the target concentration data and the target clinical data can be more effectively combined to obtain the target data. This data combination not only enhances the richness of information, but also provides good support for obtaining reliable target evaluation results in the future. On this basis, the oxidative phosphorylation degree of the target object is evaluated using the target data according to the degree analysis model, which can fully rely on the target association established between the blood molecular marker and the clinical index to ensure that the degree analysis model can use comprehensive and relevant data during analysis, so that the target evaluation result finally generated is more reliable. In addition, by combining data based on the target association relationship between the blood molecular marker and the clinical index, so as to analyze the degree of oxidative phosphorylation, a multi-dimensional data perspective can be provided, so that a more comprehensive and detailed analysis can be performed when evaluating the degree of oxidative phosphorylation, which provides good support for improving the reliability of the target evaluation result of the target object. It also solves the problem that when the blood molecular markers and clinical indicators are used to evaluate the degree of oxidative phosphorylation in the related technology, the blood molecular markers and clinical indicators are simply directly integrated without considering the correlation between them, thus resulting in low reliability in evaluating the degree of oxidative phosphorylation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A schematic flow chart of a method for evaluating oxidative phosphorylation in combination with blood molecular markers provided in an embodiment of the present invention;

[0020] Figure 2 A schematic diagram of the module structure of an oxidative phosphorylation evaluation system combined with blood molecular markers provided by an embodiment of the present invention;

[0021] Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0024] It should be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0025] The embodiment of the present invention provides a method and system for evaluating oxidative phosphorylation in combination with blood molecular markers. The method for evaluating oxidative phosphorylation in combination with blood molecular markers can be applied to a terminal device, which can be an electronic device such as a tablet computer, a laptop computer, a desktop computer, a personal digital assistant, and a wearable device. The terminal device can be a server or a server cluster.

[0026] Oxidative phosphorylation is a metabolic pathway of cells. This process occurs on the inner membrane of mitochondria of eukaryotes or the cell membrane of prokaryotes, using enzymes therein and the energy released by the oxidation of various nutrients to synthesize adenosine triphosphate. In theory, tumor cells have a higher level of glycolysis than normal cells, suggesting that the level of oxidative phosphorylation may be low in tumor cells. However, recent studies in the field of hematological tumors have shown that tumor growth requires a higher level of glycolysis, and the level of oxidative phosphorylation may also be reduced. Therefore, it is of certain practical significance to combine blood molecular markers with clinical indicators to qualitatively and quantitatively evaluate the degree of oxidative phosphorylation. However, in the related art, when using blood molecular markers and clinical indicators to evaluate the degree of oxidative phosphorylation, the blood molecular markers and clinical indicators are only directly merged without considering the correlation between them, resulting in the problem of low reliability in evaluating the degree of oxidative phosphorylation.

[0027] To this end, the present invention obtains the target concentration data corresponding to the blood molecular markers of the target object and the target clinical data corresponding to the clinical indicators of the target object, and then determines the target correlation relationship between the blood molecular markers and the clinical indicators according to expert experience or historical experience. After determining the target correlation relationship, the target concentration data can be more effectively combined with the target clinical data to obtain the target data. This data combination not only enhances the richness of information, but also provides good support for obtaining reliable target evaluation results in the future. On this basis, the oxidative phosphorylation degree of the target object is evaluated using the target data according to the degree analysis model, and the target correlation established between the blood molecular markers and the clinical indicators can be fully relied on to ensure that the degree analysis model can use comprehensive and relevant data during analysis, so that the target evaluation results finally generated are more reliable. In addition, by combining data based on the target correlation relationship between the blood molecular markers and the clinical indicators, the degree of oxidative phosphorylation is analyzed, which can provide a multi-dimensional data perspective so that a more comprehensive and detailed analysis can be performed when evaluating the degree of oxidative phosphorylation, which provides good support for improving the reliability of the target evaluation results of the target object. It also solves the problem that when the blood molecular markers and clinical indicators are used to evaluate the degree of oxidative phosphorylation in the related technology, the blood molecular markers and clinical indicators are simply directly integrated without considering the correlation between them, thus resulting in low reliability in evaluating the degree of oxidative phosphorylation.

[0028] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0029] Please refer to Figure 1 , Figure 1 A schematic flow chart of a method for evaluating oxidative phosphorylation in combination with blood molecular markers provided in an embodiment of the present invention.

[0030] like Figure 1 As shown, the method for evaluating oxidative phosphorylation in combination with blood molecular markers includes steps S101 to S104.

[0031] Step S101: Obtain target concentration data corresponding to blood molecular markers of a target object and target clinical data corresponding to clinical indicators of the target object.

[0032] Exemplarily, blood molecular markers refer to molecules present in the blood that can reflect physiological or pathological states. Blood molecular markers can be proteins, nucleic acids, metabolites or other small molecules, and are commonly used for early diagnosis, prognosis assessment and efficacy monitoring of diseases. Blood molecular markers include but are not limited to protein markers, nucleic acid markers, metabolite markers, small molecule markers, lipids and inflammatory markers, etc. Thus, the information corresponding to the blood molecular markers corresponding to the target object is measured to obtain the target concentration data corresponding to each blood molecular marker.

[0033] Exemplarily, clinical indicators include, but are not limited to, the target subject's corresponding age, gender, body mass index, underlying diseases, biochemical indicators such as blood sugar, blood lipids and other routine biochemical examination results, etc., so as to collect information on the target subject's clinical indicators and obtain corresponding target clinical data.

[0034] Step S102: determining the target correlation relationship between the blood molecular marker and the clinical indicator.

[0035] Exemplarily, blood molecular markers contain multiple parameters, and clinical indicators also contain multiple parameters. In order to ensure the accuracy of subsequent assessment of the degree of oxidative phosphorylation, when using the degree analysis model for analysis, the target correlation relationship between blood molecular markers and clinical indicators can be determined based on expert experience or historical experience, thereby providing good support for ensuring the accuracy of subsequent assessment of the degree of oxidative phosphorylation.

[0036] For example, if the blood molecular markers include the first marker and the second marker, and the clinical indicators include the first indicator and the second indicator, it can be determined based on expert experience or historical experience that the first marker and the first indicator are strongly correlated when evaluating the degree of oxidative phosphorylation, or that the second marker and the second indicator are weakly correlated when evaluating the degree of oxidative phosphorylation, and so on.

[0037] In some embodiments, determining the target association relationship between the blood molecular marker and the clinical indicator includes: obtaining initial concentration data corresponding to the blood molecular marker of a reference object, initial clinical data corresponding to the clinical indicator, and a first evaluation result corresponding to the oxidative phosphorylation degree of the reference object; determining the normal concentration range corresponding to each concentration parameter in the blood molecular marker and the normal clinical range corresponding to each clinical parameter in the clinical indicator; performing state identification on the initial concentration data according to the normal concentration range to obtain corresponding first state data and performing state identification on the initial clinical data according to the normal clinical range to obtain corresponding second state data; performing relationship identification on the first state data, the second state data, and the first evaluation result to obtain the target association relationship between the blood molecular marker and the clinical indicator.

[0038] Exemplarily, initial concentration data corresponding to blood molecular markers, initial clinical data corresponding to clinical indicators, and a first evaluation result corresponding to the oxidative phosphorylation degree of the reference subject are obtained based on historical medical records. The reference subject is a patient whose first evaluation result corresponding to the oxidative phosphorylation degree has been determined to be normal or abnormal.

[0039] Exemplarily, the normal concentration range corresponding to each concentration parameter in the blood molecular marker and the normal clinical range corresponding to each clinical parameter in the clinical indicator are determined based on expert experience or historical experience.

[0040] Exemplarily, the initial concentration data is state-identified according to the normal concentration range corresponding to each concentration parameter in the blood molecule marker. When the initial concentration data is outside the normal concentration range, the first state data corresponding to the blood molecule marker is determined to be an abnormal state. When the initial concentration data is within the normal concentration range, the first state data corresponding to the blood molecule marker is determined to be a normal state.

[0041] Exemplarily, the status of the initial clinical data is identified according to the normal clinical range corresponding to each clinical parameter in the clinical indicator. When the initial clinical data is outside the normal clinical range, the second status data corresponding to the clinical indicator is determined to be an abnormal state. When the initial clinical data is within the normal clinical range, the second status data corresponding to the clinical indicator is determined to be a normal state.

[0042] Exemplarily, when the first evaluation result is obtained as an abnormal degree, the first state data is also in an abnormal state, and the first frequency occurs. Similarly, when the first evaluation result is obtained as an abnormal degree, the second state data is also in an abnormal state, and the second frequency occurs. Then, when the first evaluation result is obtained as an abnormal degree, the third frequency is obtained when both the first state data and the second state data are in an abnormal state. Thus, the correlation between the blood molecular marker and the clinical indicator is determined according to the first frequency, the second frequency, and the third frequency, and then the target association relationship between the blood molecular marker and the clinical indicator is determined according to the correlation.

[0043] For example, the first state data is the state corresponding to the protein marker in the blood molecular marker, and the second state data is the state corresponding to the biochemical index in the clinical index. When the first evaluation result is obtained as the abnormal degree, the first state data of the protein marker is also the first frequency that appears when the abnormal state occurs. Similarly, when the first evaluation result is obtained as the abnormal degree, the second state data of the biochemical index is also the second frequency that appears when the abnormal state occurs. Then, the third frequency is obtained when the first evaluation result is the abnormal degree and the first state data of the protein marker and the second state data of the biochemical index are both abnormal states. Thus, the first frequency and the second frequency are multiplied and then divided by the first frequency and then the difference is calculated with the third frequency, and then the calculation result is determined as the correlation between the protein marker and the biochemical index. Then, when the correlation is greater than the preset value, it is determined that there is a target correlation relationship between the protein marker and the biochemical index; when the correlation is less than or equal to the preset value, it is determined that there is no target correlation relationship between the protein marker and the biochemical index. The target correlation relationship can be positive or negative.

[0044] In some embodiments, the relationship identification is performed based on the first state data, the second state data, and the first evaluation result to obtain the target association relationship between the blood molecule marker and the clinical indicator, including: obtaining a first parameter and a second parameter from the blood molecule marker and the clinical indicator, and obtaining a third state data corresponding to the first parameter and a fourth state data corresponding to the second parameter from the first state data and the second state data; obtaining a second evaluation result of the first parameter and the second parameter corresponding to the third state data and the fourth state data from the first evaluation result; determining a first abnormal probability corresponding to an abnormal degree of oxidative phosphorylation when the first parameter is determined based on the third state data and the second evaluation result; determining a second abnormal probability corresponding to an abnormal degree of oxidative phosphorylation when the second parameter is determined based on the fourth state data and the second evaluation result; determining a second abnormal probability corresponding to an abnormal degree of oxidative phosphorylation when the second parameter is determined based on the third state data and the fourth evaluation result; determining a second abnormal probability corresponding to an abnormal degree of oxidative phosphorylation when the second parameter is determined based on the third state data and the fourth evaluation result. The data and the second evaluation result determine the third abnormal probability corresponding to when the first parameter and the second parameter together make the degree of oxidative phosphorylation the abnormal degree; determine the fourth abnormal probability corresponding to when the first parameter makes the degree of oxidative phosphorylation the abnormal degree but the fourth state data corresponding to the second parameter is a normal state according to the third state data, the fourth state data and the second evaluation result; determine the fifth abnormal probability corresponding to when the second parameter makes the degree of oxidative phosphorylation the abnormal degree but the third state data corresponding to the first parameter is a normal state according to the third state data, the fourth state data and the second evaluation result; determine the initial correlation relationship between the first parameter and the second parameter according to the first abnormal probability, the second abnormal probability, the third abnormal probability, the fourth abnormal probability and the fifth abnormal probability; determine the target correlation relationship between the blood molecular marker and the clinical indicator according to the initial correlation relationship.

[0045] Exemplarily, the first parameter and the second parameter are obtained from blood molecular markers and clinical indicators, that is, the first parameter can be derived from blood molecular markers or clinical indicators, and the second parameter can be derived from blood molecular markers or clinical indicators but is different from the first parameter. In other words, the first parameter and the second parameter are any two parameters of blood molecular markers and clinical indicators, and can be derived from blood molecular markers and clinical indicators at the same time, or one can be a blood molecular marker and the other can be a clinical indicator.

[0046] Exemplarily, third state data corresponding to the first parameter and fourth state data corresponding to the second parameter are obtained from the first state data and the second state data.

[0047] Exemplarily, a second evaluation result corresponding to the first parameter and the second parameter is obtained from the first evaluation result when the third state data is abnormal or the fourth state data is abnormal and when the first evaluation result is abnormal degree.

[0048] Exemplarily, the first parameter is determined according to a statistical algorithm using the third state data and the second evaluation result so that the first abnormal probability corresponding to the abnormal degree of oxidative phosphorylation is obtained, that is, when the third state data corresponding to the first parameter is abnormal, the first quantity corresponding to the abnormal degree of the second evaluation result is obtained, and then the first abnormal probability is determined based on the first quantity divided by the total quantity corresponding to the second evaluation result.

[0049] Similarly, the second parameter is determined according to the statistical algorithm using the fourth state data and the second evaluation result so that the second abnormal probability corresponding to the abnormal degree of oxidative phosphorylation is obtained, that is, when the fourth state data corresponding to the second parameter is in an abnormal state, the second number corresponding to the second evaluation result is abnormal, and then the second abnormal probability is determined based on the second number divided by the total number corresponding to the second evaluation result.

[0050] Exemplarily, the third state data, the fourth state data and the second evaluation result are used to determine the third abnormal probability corresponding to the abnormal degree of oxidative phosphorylation when the first parameter and the second parameter are combined according to the statistical algorithm. That is, when the third state data corresponding to the first parameter is in an abnormal state, and the fourth state data corresponding to the second parameter is also in an abnormal state, the third quantity corresponding to the abnormal degree of the second evaluation result is determined by dividing the third quantity by the total quantity corresponding to the second evaluation result.

[0051] Exemplarily, the third state data, the fourth state data and the second evaluation result are used to determine the fourth abnormal probability corresponding to the first parameter when the degree of oxidative phosphorylation is abnormal but the fourth state data corresponding to the second parameter is normal according to the statistical algorithm. That is, when the third state data corresponding to the first parameter is abnormal, when the fourth state data corresponding to the second parameter is normal, the fourth quantity corresponding to the second evaluation result is abnormal, and then the fourth abnormal probability is determined by dividing the fourth quantity by the total quantity corresponding to the second evaluation result.

[0052] Exemplarily, the third state data, the fourth state data and the second evaluation result are used to determine the fifth abnormal probability corresponding to the second parameter when the degree of oxidative phosphorylation is abnormal but the third state data corresponding to the first parameter is normal according to the statistical algorithm. That is, when the fourth state data corresponding to the second parameter is abnormal, when the third state data corresponding to the first parameter is normal, the fifth quantity corresponding to the second evaluation result is abnormal, and then the fifth abnormal probability is determined by dividing the fifth quantity by the total quantity corresponding to the second evaluation result.

[0053] Exemplarily, the first abnormal probability, the second abnormal probability, the third abnormal probability, the fourth abnormal probability and the fifth abnormal probability are combined to form an initial vector for relationship identification between the first parameter and the second parameter, so as to classify the initial vector using the relationship classification model, thereby obtaining a relationship classification result, and determining the initial association relationship between the first parameter and the second parameter according to the association relationship with the maximum probability in the relationship classification result. When the association relationship with the maximum probability in the relationship classification result is no relationship, the initial association relationship between the first parameter and the second parameter is determined to be non-relationship.

[0054] Exemplarily, the above steps are performed on any two or more parameters in blood molecular markers and clinical indicators to obtain the initial correlation relationship between any two or more parameters, so that after all the initial correlation relationships are obtained, the data when the initial correlation relationship does not exist is eliminated, thereby obtaining the target correlation relationship between blood molecular markers and clinical indicators.

[0055] For example, if the initial correlation relationship between the first parameter and the second parameter is non-existent, the initial correlation relationship between the second parameter and the third parameter is positively correlated, and the initial correlation relationship between the first parameter and the third parameter is negatively correlated, then after deleting the initial correlation relationship between the first parameter and the second parameter as non-existent, the initial correlation relationship between the second parameter and the third parameter as positively correlated and the initial correlation relationship between the first parameter and the third parameter as negatively correlated are determined as the target correlation relationship between the blood molecular marker and the clinical indicator.

[0056] In some embodiments, the determining the initial association relationship between the first parameter and the second parameter according to the first abnormal probability, the second abnormal probability, the third abnormal probability, the fourth abnormal probability, and the fifth abnormal probability includes: determining a first correlation value and a second correlation value between the first parameter and the second parameter according to the first abnormal probability, the second abnormal probability, and the third abnormal probability; determining a first influence degree of the first parameter on the second parameter according to the third abnormal probability and the fourth abnormal probability; determining a second influence degree of the second parameter on the first parameter according to the third abnormal probability and the fifth abnormal probability; determining a first misleading degree of the first parameter on the second parameter according to the first abnormal probability and the fourth abnormal probability; determining a second misleading degree of the second parameter on the first parameter according to the second abnormal probability and the fifth abnormal probability; determining a first association weight corresponding to the first parameter for the second parameter according to the first correlation value, the first influence degree, and the first misleading degree; determining a second association weight corresponding to the second parameter for the first parameter according to the second correlation value, the second influence degree, and the second misleading degree; determining the initial association relationship between the first parameter and the second parameter according to the first association weight and the second association weight.

[0057] Exemplarily, the first abnormal probability and the second abnormal probability are multiplied to obtain a first product value, the first product value is divided by the first abnormal probability to obtain a first numerical value, and the second product value is divided by the second abnormal probability to obtain a second numerical value, then the first numerical value is subtracted from the third abnormal probability to obtain a first correlation value, and the second numerical value is subtracted from the third abnormal probability to obtain a second correlation value.

[0058] Exemplarily, the third abnormal probability is multiplied by 2 and then subtracted from the fourth abnormal probability to obtain the first influence degree of the first parameter on the second parameter, that is, the influence of the first parameter on the second parameter. And the third abnormal probability is multiplied by 2 and then subtracted from the fifth abnormal probability to obtain the second influence degree of the second parameter on the first parameter, that is, the influence of the second parameter on the first parameter.

[0059] Exemplarily, the first abnormal probability is subtracted from 1 to obtain a first difference, and the third abnormal probability is subtracted from the fourth abnormal probability to obtain a third difference, and the third difference is divided by the first difference to obtain a first degree of misleading of the first parameter to the second parameter, that is, the first degree of misleading is the degree of error when analyzing the degree of influence of the first parameter on the second parameter.

[0060] Exemplarily, the second abnormal probability is subtracted from 1 to obtain the second difference, and the third abnormal probability is subtracted from the fifth abnormal probability to obtain the fourth difference, and the fourth difference is divided by the second difference to obtain the second degree of misleading of the second parameter to the first parameter, that is, the second degree of misleading is the degree of error when analyzing the degree of influence of the second parameter on the first parameter.

[0061] Exemplarily, a first correlation weight corresponding to the first parameter to the second parameter is obtained by weighted summing up the first correlation value, the first influence degree and the first misleading degree, wherein the first misleading degree is the error degree when analyzing the correlation degree of the first parameter to the second parameter, and the first misleading degree needs to be subtracted during the weighted summing up to obtain the first correlation weight corresponding to the first parameter to the second parameter. Similarly, a second correlation weight corresponding to the second parameter to the first parameter is obtained by weighted summing up the second correlation value, the second influence degree and the second misleading degree; wherein the second misleading degree is the error degree when analyzing the correlation degree of the second parameter to the first parameter, and the second misleading degree needs to be subtracted during the weighted summing up to obtain the second correlation weight corresponding to the second parameter to the first parameter.

[0062] Exemplarily, when calculating the initial association relationship between the first parameter and the second parameter, the present application analyzes the influence of the first parameter on the second parameter and the influence of the second parameter on the first parameter, and then sums and averages the first association weight and the second association weight. When the average value is greater than the preset weight, the initial association relationship between the first parameter and the second parameter is that there is an association relationship. When the average value is less than or equal to the preset weight, the first association weight and the second association weight are compared with the preset weight. When the first association weight is greater than the preset weight, it is determined that the first parameter has an influence on the second parameter; when the second association weight is greater than the preset weight, it is determined that the second parameter has an influence on the first parameter, so that when the first association weight is greater than the preset weight, but the second association weight is less than or equal to the preset weight, it is determined that the first parameter has an influence on the second parameter, but the second parameter has no influence on the first parameter. Similarly, when the second association weight is greater than the preset weight, but the first association weight is less than or equal to the preset weight, it is determined that the second parameter has an influence on the first parameter, but the first parameter has no influence on the second parameter.

[0063] In other words, when the initial association relationship analysis is performed on the first parameter and the second parameter according to the first association weight and the second association weight, the relationship between the first parameter and the second parameter may be a bidirectional relationship or a unidirectional relationship.

[0064] Step S103: combining the target concentration data and the target clinical data according to the target association relationship to obtain target data corresponding to the target object.

[0065] Exemplarily, the target parameter corresponding to the target association relationship is obtained, and then the data corresponding to each parameter in the target parameter is obtained from the target concentration data and the target clinical data, so that the data corresponding to each parameter is combined to obtain the target data corresponding to the target object. The target association relationship may include multiple association relationships, so the obtained target data may also include multiple data combinations.

[0066] For example, if the target association relationship includes a first association relationship and a second association relationship, the first association parameter corresponding to the first association relationship is obtained, and then the data corresponding to each sub-parameter in the first association parameter is obtained from the target concentration data and the target clinical data, and then the data corresponding to each sub-parameter is combined to obtain the first relationship data corresponding to the target object. And the second association parameter corresponding to the second association relationship is obtained, and then the data corresponding to each sub-parameter in the second association parameter is obtained from the target concentration data and the target clinical data, and then the data corresponding to each sub-parameter is combined to obtain the second relationship data corresponding to the target object, and then the first relationship data and the second relationship data are both determined as target data.

[0067] In some embodiments, combining the target concentration data and the target clinical data according to the target association relationship to obtain the target data corresponding to the target object includes: determining a first target weight corresponding to the first parameter in the target association relationship and a second target weight corresponding to the second parameter according to the first association weight and the second association weight; determining a third target weight corresponding to the blood molecular marker and the remaining parameters in the clinical indicators according to the target association relationship; combining the target concentration data and the target clinical data according to the first target weight, the second target weight and the third target weight to obtain the target data corresponding to the target object.

[0068] Exemplarily, when the initial association relationship is an existing association relationship, the initial association relationship will be retained in the target association relationship, and then normalized according to the first association weight of the first parameter and the second association weight of the second parameter in the initial association relationship to obtain the first target weight corresponding to the first parameter and the second target weight corresponding to the second parameter.

[0069] Exemplarily, considering that the remaining parameters in the blood molecular markers and clinical indicators other than the first parameter and the second parameter also have an impact on the evaluation of the degree of oxidative phosphorylation, the third target weight corresponding to the remaining parameters in the blood molecular markers and clinical indicators is determined according to expert experience or historical experience. That is, when evaluating the degree of oxidative phosphorylation based on the target association relationship, not only the influence of the first parameter and the second parameter in the target association relationship on the degree of oxidative phosphorylation should be considered, but also the influence of the remaining parameters in the blood molecular markers and clinical indicators other than the first parameter and the second parameter on the degree of oxidative phosphorylation should be paid attention to. In order to avoid erroneous interpretation or deviation caused by over-reliance on one or two parameters, the remaining parameters can also be considered when considering the target association relationship between the first parameter and the second parameter, thereby providing support for the subsequent improvement of the accuracy and reliability of the target evaluation results.

[0070] Exemplarily, first data corresponding to the first parameter, second data corresponding to the second parameter, and third data corresponding to the remaining parameters are obtained from the target concentration data and the target clinical data, and then the first target weight, the second target weight, and the third target weight as well as the first data, the second data, and the third data are jointly determined as target data, so that after the degree analysis model obtains the target data, it can configure different levels of importance when analyzing the degree of oxidative phosphorylation according to the target weights of different parameters.

[0071] Step S104: evaluating the degree of oxidative phosphorylation corresponding to the target object using the target data according to the degree analysis model to obtain a target evaluation result corresponding to the target object.

[0072] Exemplarily, the target data corresponding to the target association relationship is input into the oxidative phosphorylation degree classification layer corresponding to the target association relationship in the degree analysis model, and then the initial classification result corresponding to the target data under the target association relationship is obtained. Thus, the target classification result is obtained by voting based on multiple initial classification results, and then the target evaluation result corresponding to the target object is determined based on the target classification result.

[0073] In some embodiments, the degree of oxidative phosphorylation corresponding to the target object is evaluated using the target data according to the degree analysis model to obtain a target evaluation result corresponding to the target object, including: evaluating the degree of oxidative phosphorylation of the target data using the data classification layer of the degree analysis model to obtain initial distribution information; performing reliability screening on the initial distribution information using the distribution screening layer of the degree analysis model to obtain target distribution information; performing probability fusion on the target distribution information using the evidence fusion layer of the target analysis model to obtain a target evaluation distribution corresponding to the target object; and determining the target evaluation result corresponding to the target object according to the target evaluation distribution.

[0074] Exemplarily, the data classification layers of the degree analysis model are used to evaluate the degree of oxidative phosphorylation of the target data to obtain initial distribution information, wherein the number of data classification layers is the same as the number corresponding to the target association relationship.

[0075] For example, when the first target weight, the second target weight and the third target weight as well as the first data, the second data and the third data are jointly determined as target data, then when training the data classification layer of the degree analysis model, it is necessary to consider the target weights corresponding to different parameters, so that after obtaining the data classification layer of the degree analysis model, the first data, the second data and the third data are input into the data classification layer in sequence for oxidative phosphorylation degree evaluation, thereby obtaining the initial distribution information of the target object under the target association relationship.

[0076] Exemplarily, the distribution screening layer of the degree analysis model is used to compare the maximum probability in the initial distribution information with the preset probability. When the maximum probability is greater than or equal to the preset probability, the initial distribution information is retained; when the maximum probability is less than the preset probability, the initial distribution information is discarded. Thus, the retained initial distribution information is determined as the target distribution information.

[0077] Exemplarily, the target distribution information is probabilistically fused using the evidence theory algorithm in the evidence fusion layer of the target analysis model to obtain the target evaluation distribution corresponding to the target object. Finally, the evaluation type corresponding to the maximum distribution probability in the target evaluation distribution is determined as the target evaluation result corresponding to the target object.

[0078] For example, when the distribution probability in the target evaluation distribution is the largest, the corresponding evaluation type is normal, then the target evaluation result corresponding to the target object is determined to be normal, that is, the oxidative phosphorylation degree corresponding to the target object is normal; when the distribution probability in the target evaluation distribution is the largest, the corresponding evaluation type is abnormal, then the target evaluation result corresponding to the target object is determined to be abnormal, that is, the oxidative phosphorylation degree corresponding to the target object is abnormal.

[0079] In addition, the evaluation types in the target evaluation distribution may include normal, abnormal, normal, slightly abnormal, severely abnormal, etc. The specific evaluation type may be selected according to actual needs and is not limited in this application.

[0080] In some embodiments, the use of the distribution screening layer of the degree analysis model to perform reliability screening on the initial distribution information to obtain the target distribution information includes: obtaining the target association weight corresponding to the target association relationship according to the distribution screening layer; performing distribution reliability calculation on the initial distribution information according to the distribution screening layer to obtain the target reliability value corresponding to the initial distribution information; and screening the initial distribution information using the target association weight and the target reliability value according to the distribution screening layer to obtain the target distribution information; wherein the target reliability value is obtained according to the following formula:

[0081]

[0082] Among them, con i represents the target reliability value corresponding to the i-th initial distribution information, N represents the number of classifications corresponding to the degree of oxidative phosphorylation in the initial distribution information, and p ij Represents the probability information of the i-th initial distribution information at the j-th oxidative phosphorylation level.

[0083] Exemplarily, the target association weight corresponding to the target association relationship is obtained according to the distribution screening layer, that is, the reliability corresponding to the target association relationship is obtained. For example, the target association weight corresponding to the target association relationship is determined as the average value corresponding to the first association weight and the second association weight, and can also be set according to expert experience or actual needs.

[0084] Exemplarily, the distribution screening layer is used to calculate the distribution reliability of the initial distribution information according to the following formula to obtain the target reliability value corresponding to the initial distribution information:

[0085]

[0086] Among them, con i represents the target reliability value corresponding to the i-th initial distribution information, N represents the number of classifications corresponding to the degree of oxidative phosphorylation in the initial distribution information, and p ij Represents the probability information of the i-th initial distribution information at the j-th oxidative phosphorylation level.

[0087] Exemplarily, the larger the probability information gap corresponding to each oxidative phosphorylation degree in the initial distribution information, the larger the target reliability value, and the smaller the probability information gap corresponding to each oxidative phosphorylation degree in the initial distribution information, the smaller the target reliability value. Thus, when the target reliability value is larger, the larger the probability information gap corresponding to each oxidative phosphorylation degree in the initial distribution information, the more accurate the initial distribution information is for identifying the oxidative phosphorylation degree of the target object.

[0088] Exemplarily, the target reliability value is compared with the preset reliability value according to the distribution screening layer. When the target reliability value is greater than or equal to the preset reliability value, the initial distribution information is retained. When the target reliability value is less than the preset reliability value, the size between the target association weight and the preset weight continues to be judged. When the target association weight is greater than or equal to the preset weight, the initial distribution information is retained. Otherwise, when the target association weight is less than the preset weight, the initial distribution information is deleted. Finally, the retained initial distribution information is determined as the target distribution information.

[0089] The embodiment of the present invention provides an oxidative phosphorylation evaluation method and system combined with a blood molecular marker, the method comprising: obtaining target concentration data corresponding to the blood molecular marker of the target object and target clinical data corresponding to the clinical index of the target object, and then determining the target association relationship between the blood molecular marker and the clinical index according to expert experience or historical experience. After determining the target association relationship, the target concentration data and the target clinical data can be more effectively combined to obtain the target data. This data combination not only enhances the richness of information, but also provides good support for obtaining reliable target evaluation results in the future. On this basis, the oxidative phosphorylation degree of the target object is evaluated using the target data according to the degree analysis model, which can fully rely on the target association established between the blood molecular marker and the clinical index to ensure that the degree analysis model can use comprehensive and relevant data during analysis, so that the target evaluation result finally generated is more reliable. In addition, by combining data based on the target association relationship between the blood molecular marker and the clinical index, so as to analyze the degree of oxidative phosphorylation, a multi-dimensional data perspective can be provided, so that a more comprehensive and detailed analysis can be performed when evaluating the degree of oxidative phosphorylation, which provides good support for improving the reliability of the target evaluation result of the target object. It also solves the problem that when the blood molecular markers and clinical indicators are used to evaluate the degree of oxidative phosphorylation in the related technology, the blood molecular markers and clinical indicators are simply directly integrated without considering the correlation between them, thus resulting in low reliability in evaluating the degree of oxidative phosphorylation.

[0090] See also Figure 2 , Figure 2An oxidative phosphorylation evaluation system 200 combined with blood molecular markers is provided in an embodiment of the present application. The oxidative phosphorylation evaluation system 200 combined with blood molecular markers includes a data acquisition module 201, a relationship determination module 202, a data combination module 203, and a data evaluation module 204, wherein the data acquisition module 201 is used to obtain target concentration data corresponding to the blood molecular markers of a target object and target clinical data corresponding to the clinical indicators of the target object; the relationship determination module 202 is used to determine the target association relationship between the blood molecular markers and the clinical indicators; the data combination module 203 is used to combine the target concentration data and the target clinical data according to the target association relationship to obtain the target data corresponding to the target object; the data evaluation module 204 is used to evaluate the oxidative phosphorylation degree corresponding to the target object using the target data according to the degree analysis model, and obtain the target evaluation result corresponding to the target object.

[0091] In some embodiments, the relationship determination module 202 performs, during the process of determining the target association relationship between the blood molecular marker and the clinical indicator:

[0092] Obtaining initial concentration data corresponding to the blood molecular marker of the reference subject, initial clinical data corresponding to the clinical index, and a first evaluation result corresponding to the oxidative phosphorylation degree of the reference subject;

[0093] Determining the normal concentration range corresponding to each concentration parameter in the blood molecular marker and the normal clinical range corresponding to each clinical parameter in the clinical indicator;

[0094] Performing status identification on the initial concentration data according to the normal concentration range to obtain corresponding first status data and performing status identification on the initial clinical data according to the normal clinical range to obtain corresponding second status data;

[0095] Relationship identification is performed based on the first state data, the second state data and the first evaluation result to obtain the target association relationship between the blood molecular marker and the clinical indicator.

[0096] In some embodiments, the relationship determination module 202, in the process of performing relationship identification according to the first state data, the second state data and the first evaluation result to obtain the target association relationship between the blood molecular marker and the clinical indicator, executes:

[0097] Obtaining a first parameter and a second parameter from the blood molecular markers and the clinical indicators, and obtaining third state data corresponding to the first parameter and fourth state data corresponding to the second parameter from the first state data and the second state data;

[0098] Obtaining, from the first evaluation result, a second evaluation result in which the first parameter and the second parameter correspond to the third state data and the fourth state data;

[0099] Determine, according to the third state data and the second evaluation result, a first abnormal probability corresponding to when the first parameter makes the degree of oxidative phosphorylation abnormal;

[0100] Determine the second parameter according to the fourth state data and the second evaluation result so that a second abnormal probability corresponding to the oxidative phosphorylation degree is the abnormal degree;

[0101] Determine, according to the third state data, the fourth state data and the second evaluation result, a third abnormal probability corresponding to when the first parameter and the second parameter together make the degree of oxidative phosphorylation the abnormal degree;

[0102] Determine, according to the third state data, the fourth state data and the second evaluation result, a fourth abnormal probability corresponding to the case where the first parameter makes the degree of oxidative phosphorylation the abnormal degree but the fourth state data corresponding to the second parameter is a normal state;

[0103] Determine, according to the third state data, the fourth state data and the second evaluation result, the fifth abnormal probability corresponding to the second parameter when the degree of oxidative phosphorylation is the abnormal degree but the third state data corresponding to the first parameter is the normal state;

[0104] Determine an initial correlation relationship between the first parameter and the second parameter according to the first abnormal probability, the second abnormal probability, the third abnormal probability, the fourth abnormal probability, and the fifth abnormal probability;

[0105] The target correlation relationship between the blood molecular marker and the clinical indicator is determined according to the initial correlation relationship.

[0106] In some implementations, the relationship determination module 202, in the process of determining the initial association relationship between the first parameter and the second parameter according to the first abnormal probability, the second abnormal probability, the third abnormal probability, the fourth abnormal probability, and the fifth abnormal probability, performs:

[0107] Determine a first correlation value and a second correlation value between the first parameter and the second parameter according to the first abnormal probability, the second abnormal probability and the third abnormal probability;

[0108] Determine a first influence degree of the first parameter on the second parameter according to the third abnormal probability and the fourth abnormal probability;

[0109] Determine a second degree of influence of the second parameter on the first parameter according to the third abnormal probability and the fifth abnormal probability;

[0110] Determine a first misleading degree of the first parameter to the second parameter according to the first abnormal probability and the fourth abnormal probability;

[0111] Determine a second misleading degree of the first parameter corresponding to the second parameter according to the second abnormal probability and the fifth abnormal probability;

[0112] Determine a first association weight corresponding to the first parameter to the second parameter according to the first correlation value, the first influence degree, and the first misleading degree;

[0113] Determine a second association weight corresponding to the second parameter to the first parameter according to the second correlation value, the second influence degree, and the second misleading degree;

[0114] The initial association relationship between the first parameter and the second parameter is determined according to the first association weight and the second association weight.

[0115] In some embodiments, the data combination module 203 performs, during the process of combining the target concentration data and the target clinical data according to the target association relationship to obtain the target data corresponding to the target object:

[0116] Determine, according to the first association weight and the second association weight, a first target weight corresponding to the first parameter and a second target weight corresponding to the second parameter in the target association relationship;

[0117] Determine, according to the target association relationship, a third target weight corresponding to the blood molecular marker and the remaining parameters in the clinical index;

[0118] The target concentration data and the target clinical data are combined according to the first target weight, the second target weight, and the third target weight to obtain the target data corresponding to the target object.

[0119] In some embodiments, the data evaluation module 204, in the process of evaluating the oxidative phosphorylation degree corresponding to the target object using the target data according to the degree analysis model to obtain the target evaluation result corresponding to the target object, performs:

[0120] Using the data classification layer of the degree analysis model to evaluate the degree of oxidative phosphorylation of the target data, and obtain initial distribution information;

[0121] Using the distribution screening layer of the degree analysis model to perform reliability screening on the initial distribution information to obtain target distribution information;

[0122] Using the evidence fusion layer of the target analysis model to perform probability fusion on the target distribution information to obtain the target evaluation distribution corresponding to the target object;

[0123] The target evaluation result corresponding to the target object is determined according to the target evaluation distribution.

[0124] In some implementations, the data evaluation module 204 performs, during the process of performing reliability screening on the initial distribution information using the distribution screening layer of the degree analysis model to obtain target distribution information:

[0125] Obtaining a target association weight corresponding to the target association relationship according to the distribution screening layer;

[0126] Performing distribution reliability calculation on the initial distribution information according to the distribution screening layer to obtain a target reliability value corresponding to the initial distribution information;

[0127] The target distribution information is obtained by filtering the initial distribution information using the target association weight and the target reliability value according to the distribution screening layer;

[0128] The target reliability value is obtained according to the following formula:

[0129]

[0130] Among them, con i represents the target reliability value corresponding to the i-th initial distribution information, N represents the number of classifications corresponding to the degree of oxidative phosphorylation in the initial distribution information, and p ij Represents the probability information of the i-th initial distribution information at the j-th oxidative phosphorylation level.

[0131] In some embodiments, the oxidative phosphorylation assessment system 200 combined with blood molecular markers can be applied to a terminal device.

[0132] It should be noted that, those skilled in the art can clearly understand that, for the sake of convenience and brevity of description, the specific working process of the oxidative phosphorylation evaluation system 200 combined with blood molecular markers described above can refer to the corresponding process in the aforementioned embodiment of the oxidative phosphorylation evaluation method combined with blood molecular markers, and will not be repeated here.

[0133] See also Figure 3 , Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention.

[0134] like Figure 3 As shown, the terminal device 300 includes a processor 301 and a memory 302, and the processor 301 and the memory 302 are connected via a bus 303, such as an I2C (Inter-integrated Circuit) bus.

[0135] Specifically, the processor 301 is used to provide computing and control capabilities to support the operation of the entire terminal device. The processor 301 can be a central processing unit (CPU), and the processor 301 can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0136] Specifically, the memory 302 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.

[0137] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a partial structure related to the embodiment of the present invention, and does not constitute a limitation on the terminal device to which the embodiment of the present invention is applied. The specific server may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0138] The processor is used to run a computer program stored in the memory, and implement any one of the methods for evaluating oxidative phosphorylation combined with blood molecular markers provided by the embodiments of the present invention when executing the computer program.

[0139] In one embodiment, the processor is used to run a computer program stored in the memory, and implements the following steps when executing the computer program:

[0140] Obtaining target concentration data corresponding to the blood molecular markers of the target subject and target clinical data corresponding to the clinical indicators of the target subject;

[0141] Determining the target correlation relationship between the blood molecular marker and the clinical indicator;

[0142] Combining the target concentration data and the target clinical data according to the target association relationship to obtain target data corresponding to the target object;

[0143] The target data is used to evaluate the degree of oxidative phosphorylation corresponding to the target object according to the degree analysis model to obtain a target evaluation result corresponding to the target object.

[0144] In some embodiments, during the process of determining the target association relationship between the blood molecular marker and the clinical indicator, the processor 301 executes:

[0145] Obtaining initial concentration data corresponding to the blood molecular marker of the reference subject, initial clinical data corresponding to the clinical index, and a first evaluation result corresponding to the oxidative phosphorylation degree of the reference subject;

[0146] Determining the normal concentration range corresponding to each concentration parameter in the blood molecular marker and the normal clinical range corresponding to each clinical parameter in the clinical indicator;

[0147] Performing status identification on the initial concentration data according to the normal concentration range to obtain corresponding first status data and performing status identification on the initial clinical data according to the normal clinical range to obtain corresponding second status data;

[0148] Relationship identification is performed based on the first state data, the second state data and the first evaluation result to obtain the target association relationship between the blood molecular marker and the clinical indicator.

[0149] In some embodiments, the processor 301, in the process of performing relationship identification according to the first state data, the second state data, and the first evaluation result to obtain the target association relationship between the blood molecular marker and the clinical indicator, executes:

[0150] Obtaining a first parameter and a second parameter from the blood molecular markers and the clinical indicators, and obtaining third state data corresponding to the first parameter and fourth state data corresponding to the second parameter from the first state data and the second state data;

[0151] Obtaining, from the first evaluation result, a second evaluation result in which the first parameter and the second parameter correspond to the third state data and the fourth state data;

[0152] Determine, according to the third state data and the second evaluation result, a first abnormal probability corresponding to when the first parameter makes the degree of oxidative phosphorylation abnormal;

[0153] Determine the second parameter according to the fourth state data and the second evaluation result so that a second abnormal probability corresponding to the degree of oxidative phosphorylation is the abnormal degree;

[0154] Determine, according to the third state data, the fourth state data and the second evaluation result, a third abnormal probability corresponding to when the first parameter and the second parameter together make the degree of oxidative phosphorylation the abnormal degree;

[0155] Determine, according to the third state data, the fourth state data and the second evaluation result, a fourth abnormal probability corresponding to the case where the first parameter makes the degree of oxidative phosphorylation the abnormal degree but the fourth state data corresponding to the second parameter is a normal state;

[0156] Determine, according to the third state data, the fourth state data and the second evaluation result, the fifth abnormal probability corresponding to the second parameter when the degree of oxidative phosphorylation is the abnormal degree but the third state data corresponding to the first parameter is the normal state;

[0157] Determine an initial correlation relationship between the first parameter and the second parameter according to the first abnormal probability, the second abnormal probability, the third abnormal probability, the fourth abnormal probability, and the fifth abnormal probability;

[0158] The target correlation relationship between the blood molecular marker and the clinical indicator is determined according to the initial correlation relationship.

[0159] In some implementations, the processor 301, in the process of determining the initial association relationship between the first parameter and the second parameter according to the first abnormal probability, the second abnormal probability, the third abnormal probability, the fourth abnormal probability, and the fifth abnormal probability, executes:

[0160] Determine a first correlation value and a second correlation value between the first parameter and the second parameter according to the first abnormal probability, the second abnormal probability and the third abnormal probability;

[0161] Determine a first influence degree of the first parameter on the second parameter according to the third abnormal probability and the fourth abnormal probability;

[0162] Determine a second degree of influence of the second parameter on the first parameter according to the third abnormal probability and the fifth abnormal probability;

[0163] Determine a first misleading degree of the first parameter to the second parameter according to the first abnormal probability and the fourth abnormal probability;

[0164] Determine a second misleading degree of the first parameter corresponding to the second parameter according to the second abnormal probability and the fifth abnormal probability;

[0165] Determine a first association weight corresponding to the first parameter to the second parameter according to the first correlation value, the first influence degree, and the first misleading degree;

[0166] Determine a second association weight corresponding to the second parameter to the first parameter according to the second correlation value, the second influence degree, and the second misleading degree;

[0167] The initial association relationship between the first parameter and the second parameter is determined according to the first association weight and the second association weight.

[0168] In some embodiments, during the process of combining the target concentration data and the target clinical data according to the target association relationship to obtain the target data corresponding to the target object, the processor 301 executes:

[0169] Determine, according to the first association weight and the second association weight, a first target weight corresponding to the first parameter and a second target weight corresponding to the second parameter in the target association relationship;

[0170] Determine, according to the target association relationship, a third target weight corresponding to the blood molecular marker and the remaining parameters in the clinical index;

[0171] The target concentration data and the target clinical data are combined according to the first target weight, the second target weight, and the third target weight to obtain the target data corresponding to the target object.

[0172] In some embodiments, the processor 301, in the process of evaluating the oxidative phosphorylation degree corresponding to the target object using the target data according to the degree analysis model to obtain the target evaluation result corresponding to the target object, executes:

[0173] Using the data classification layer of the degree analysis model to evaluate the degree of oxidative phosphorylation of the target data, and obtain initial distribution information;

[0174] Using the distribution screening layer of the degree analysis model to perform reliability screening on the initial distribution information to obtain target distribution information;

[0175] Using the evidence fusion layer of the target analysis model to perform probability fusion on the target distribution information to obtain the target evaluation distribution corresponding to the target object;

[0176] The target evaluation result corresponding to the target object is determined according to the target evaluation distribution.

[0177] In some implementations, the processor 301, in the process of performing reliability screening on the initial distribution information using the distribution screening layer of the degree analysis model to obtain target distribution information, executes:

[0178] Obtaining a target association weight corresponding to the target association relationship according to the distribution screening layer;

[0179] Performing distribution reliability calculation on the initial distribution information according to the distribution screening layer to obtain a target reliability value corresponding to the initial distribution information;

[0180] The target distribution information is obtained by filtering the initial distribution information using the target association weight and the target reliability value according to the distribution screening layer;

[0181] The target reliability value is obtained according to the following formula:

[0182]

[0183] Among them, con i represents the target reliability value corresponding to the i-th initial distribution information, N represents the number of classifications corresponding to the degree of oxidative phosphorylation in the initial distribution information, and p ij Represents the probability information of the i-th initial distribution information at the j-th oxidative phosphorylation level.

[0184] It should be noted that technicians in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working process of the terminal device described above can refer to the corresponding process in the aforementioned embodiment of the oxidative phosphorylation evaluation method combined with blood molecular markers, and will not be repeated here.

[0185] An embodiment of the present invention also provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any method for evaluating oxidative phosphorylation combined with blood molecular markers as provided in the description of the embodiment of the present invention.

[0186] The storage medium may be an internal storage unit of the terminal device described in the foregoing embodiment, such as a hard disk or memory of the terminal device. The storage medium may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc., equipped on the terminal device.

[0187] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware embodiment, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0188] It should be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0189] The serial numbers of the embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. The above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for evaluating oxidative phosphorylation in combination with blood molecular markers, characterized in that: The method comprises: Obtaining target concentration data corresponding to the blood molecular markers of the target subject and target clinical data corresponding to the clinical indicators of the target subject; Determining the target correlation relationship between the blood molecular marker and the clinical indicator; Combining the target concentration data and the target clinical data according to the target association relationship to obtain target data corresponding to the target object; The target data is used to evaluate the degree of oxidative phosphorylation corresponding to the target object according to the degree analysis model to obtain a target evaluation result corresponding to the target object.

2. The method according to claim 1, characterized in that The determining of the target association relationship between the blood molecular marker and the clinical indicator comprises: Obtaining initial concentration data corresponding to the blood molecular marker of the reference subject, initial clinical data corresponding to the clinical index, and a first evaluation result corresponding to the oxidative phosphorylation degree of the reference subject; Determining the normal concentration range corresponding to each concentration parameter in the blood molecular marker and the normal clinical range corresponding to each clinical parameter in the clinical indicator; Performing status identification on the initial concentration data according to the normal concentration range to obtain corresponding first status data and performing status identification on the initial clinical data according to the normal clinical range to obtain corresponding second status data; Relationship identification is performed based on the first state data, the second state data and the first evaluation result to obtain the target association relationship between the blood molecular marker and the clinical indicator.

3. The method according to claim 2, characterized in that The performing relationship identification according to the first state data, the second state data and the first evaluation result to obtain the target association relationship between the blood molecular marker and the clinical indicator includes: Obtaining a first parameter and a second parameter from the blood molecular markers and the clinical indicators, and obtaining third state data corresponding to the first parameter and fourth state data corresponding to the second parameter from the first state data and the second state data; Obtaining, from the first evaluation result, a second evaluation result in which the first parameter and the second parameter correspond to the third state data and the fourth state data; Determine, according to the third state data and the second evaluation result, a first abnormal probability corresponding to when the first parameter makes the degree of oxidative phosphorylation abnormal; Determine the second parameter according to the fourth state data and the second evaluation result so that a second abnormal probability corresponding to the degree of oxidative phosphorylation is the abnormal degree; Determine, according to the third state data, the fourth state data and the second evaluation result, a third abnormal probability corresponding to when the first parameter and the second parameter together make the degree of oxidative phosphorylation the abnormal degree; Determine, according to the third state data, the fourth state data and the second evaluation result, a fourth abnormal probability corresponding to the case where the first parameter makes the degree of oxidative phosphorylation the abnormal degree but the fourth state data corresponding to the second parameter is a normal state; Determine, according to the third state data, the fourth state data and the second evaluation result, the fifth abnormal probability corresponding to the second parameter when the degree of oxidative phosphorylation is the abnormal degree but the third state data corresponding to the first parameter is the normal state; Determine an initial correlation relationship between the first parameter and the second parameter according to the first abnormal probability, the second abnormal probability, the third abnormal probability, the fourth abnormal probability, and the fifth abnormal probability; The target correlation relationship between the blood molecular marker and the clinical indicator is determined according to the initial correlation relationship.

4. The method according to claim 3, characterized in that The determining an initial correlation relationship between the first parameter and the second parameter according to the first abnormal probability, the second abnormal probability, the third abnormal probability, the fourth abnormal probability, and the fifth abnormal probability includes: Determine a first correlation value and a second correlation value between the first parameter and the second parameter according to the first abnormal probability, the second abnormal probability and the third abnormal probability; Determine a first influence degree of the first parameter on the second parameter according to the third abnormal probability and the fourth abnormal probability; Determine a second degree of influence of the second parameter on the first parameter according to the third abnormal probability and the fifth abnormal probability; Determine a first misleading degree of the first parameter to the second parameter according to the first abnormal probability and the fourth abnormal probability; Determine a second misleading degree of the first parameter corresponding to the second parameter according to the second abnormal probability and the fifth abnormal probability; Determine a first association weight corresponding to the first parameter to the second parameter according to the first correlation value, the first influence degree, and the first misleading degree; Determine a second association weight corresponding to the second parameter to the first parameter according to the second correlation value, the second influence degree, and the second misleading degree; The initial association relationship between the first parameter and the second parameter is determined according to the first association weight and the second association weight.

5. The method according to claim 4, characterized in that The step of combining the target concentration data and the target clinical data according to the target association relationship to obtain the target data corresponding to the target object includes: Determine, according to the first association weight and the second association weight, a first target weight corresponding to the first parameter and a second target weight corresponding to the second parameter in the target association relationship; Determine, according to the target association relationship, a third target weight corresponding to the blood molecular marker and the remaining parameters in the clinical index; The target concentration data and the target clinical data are combined according to the first target weight, the second target weight, and the third target weight to obtain the target data corresponding to the target object.

6. The method according to claim 1, characterized in that The step of evaluating the oxidative phosphorylation degree corresponding to the target object using the target data according to the degree analysis model to obtain a target evaluation result corresponding to the target object includes: Using the data classification layer of the degree analysis model to evaluate the degree of oxidative phosphorylation of the target data, and obtain initial distribution information; Using the distribution screening layer of the degree analysis model to perform reliability screening on the initial distribution information to obtain target distribution information; Using the evidence fusion layer of the target analysis model to perform probability fusion on the target distribution information to obtain the target evaluation distribution corresponding to the target object; The target evaluation result corresponding to the target object is determined according to the target evaluation distribution.

7. The method according to claim 6, characterized in that The step of using the distribution screening layer of the degree analysis model to perform reliability screening on the initial distribution information to obtain target distribution information includes: Obtaining a target association weight corresponding to the target association relationship according to the distribution screening layer; Performing distribution reliability calculation on the initial distribution information according to the distribution screening layer to obtain a target reliability value corresponding to the initial distribution information; The target distribution information is obtained by filtering the initial distribution information using the target association weight and the target reliability value according to the distribution screening layer; The target reliability value is obtained according to the following formula: Among them, con i represents the target reliability value corresponding to the i-th initial distribution information, N represents the number of classifications corresponding to the degree of oxidative phosphorylation in the initial distribution information, and p ij Represents the probability information of the i-th initial distribution information at the j-th oxidative phosphorylation level.

8. An oxidative phosphorylation evaluation system combined with blood molecular markers, characterized in that: include: A data acquisition module, used to obtain target concentration data corresponding to the blood molecular markers of the target object and target clinical data corresponding to the clinical indicators of the target object; A relationship determination module, used to determine the target association relationship between the blood molecular marker and the clinical indicator; A data combination module, used for combining the target concentration data and the target clinical data according to the target association relationship to obtain target data corresponding to the target object; The data evaluation module is used to evaluate the oxidative phosphorylation degree corresponding to the target object using the target data according to the degree analysis model to obtain a target evaluation result corresponding to the target object.

9. A terminal device, characterized in that: The terminal device includes a processor and a memory; The memory is used to store computer programs; The processor is used to execute the computer program and implement the method for evaluating oxidative phosphorylation in combination with blood molecular markers according to any one of claims 1 to 7 when executing the computer program.

10. A computer storage medium for computer storage, characterized in that: The computer storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the method for evaluating oxidative phosphorylation in combination with blood molecular markers according to any one of claims 1 to 7.

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