Gene data processing method and electronic equipment

Abstract

The invention relates to a gene data processing method and electronic equipment, and relates to the technical field of data processing, and the method comprises the following steps: identifying a mutant gene in a gene detection result of a user; determining a target disease associated with the mutant gene; at least one mutant gene is associated with the target disease; determining the illness probability of the target disease according to the number of the mutant genes associated with the target disease; wherein the number of the mutant genes associated with the target disease is positively correlated with the severity of the target disease. By using the gene data processing method provided by the invention, a gene detection result with relatively high readability can be provided, so that a user is assisted in knowing own physical conditions.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of data processing, in particular, to a gene data processing method and an electronic device. BACKGROUND

[0002] Gene detection technology is a method of obtaining genetic information by analyzing the gene sequence of an organism, which helps to understand the genetic composition, gene mutation and gene expression level of the organism, thereby providing a basis for disease diagnosis, treatment, prevention and organism medical treatment.

[0003] In the related art, a gene detection result can be obtained through gene detection technology, but non-professionals cannot understand the gene detection result, which makes it difficult for non-professionals to know their physical condition through the gene detection result. SUMMARY

[0004] The purpose of the present disclosure is to provide a gene data processing method and an electronic device to solve the above technical problems.

[0005] To achieve the above purpose, the present disclosure provides a gene data processing method, comprising: identifying a mutation gene in a gene detection result of a user; determining a target disease associated with the mutation gene; the mutation gene associated with the target disease is at least one; determining a disease probability of the target disease according to the number of mutation genes associated with the target disease; wherein the number of mutation genes associated with the target disease is positively correlated with the severity of the target disease.

[0006] Optionally, the determination of the target disease associated with the mutation gene comprises: determining the correlation between the mutation gene and a plurality of diseases; selecting a target disease with a correlation greater than a preset correlation between the mutation gene and the plurality of diseases from the plurality of diseases.

[0007] Optionally, the target disease is a plurality; the method further comprises: displaying a first interface in response to a first trigger operation applied; the first interface comprises a disease query portal; displaying a second interface in response to a second trigger operation applied to the disease query portal; the second interface comprises a disease probability effect diagram, and the disease probability effect diagram indicates a plurality of target diseases arranged from high to low in disease probability.

[0008] Optionally, the plurality of target diseases correspond to a diagnosis and treatment query portal respectively; the method further comprises: In response to a third trigger operation on the diagnosis and treatment query entry corresponding to the target disease, a third interface is displayed; the third interface is used to display diagnosis and treatment information, and the diagnosis and treatment information includes at least one of the treatment plan and cause of the target disease.

[0009] Optionally, the first interface includes a comparison query entry; and the method further includes: In response to a fourth trigger operation on the comparison query entry, a fourth interface is displayed; the fourth interface is used to display the comparison results between multiple gene test reports, and the gene test reports are generated based on the probability of suffering from the target disease.

[0010] Optionally, the first interface includes a review query entry; and the method further includes: In response to the fifth trigger operation on the review query entry, a fifth interface is displayed; the fifth interface is used to display the review detection method of the target disease whose probability of disease is greater than the preset probability.

[0011] Optionally, the method further includes: Broadcasting a genetic test report; the genetic test report is generated based on the probability of suffering from the target disease; monitoring the user's emotions during the broadcasting of the genetic test report; In a case where it is identified that the user emotion is a first preset emotion, an emotion soothing strategy corresponding to the first preset emotion is executed.

[0012] Optionally, the method further includes: Broadcasting a genetic test report; the genetic test report is generated based on the probability of suffering from the target disease; monitoring the user's emotions during the broadcasting of the genetic test report; According to the recognized user emotion, a to-be-treated symptom among a plurality of target symptoms is determined; the to-be-treated symptom is a target symptom reported when the user emotion is a second preset emotion.

[0013] Optionally, determining the probability of suffering from the target disease according to the number of mutated genes associated with the target disease includes: determining a first probability of disease occurrence of the target disease according to the number of mutated genes associated with the target disease; The first disease probability is corrected according to the user's age to obtain a second disease probability.

[0014] In order to achieve the above objectives, the present disclosure provides an electronic device, comprising: a memory having a computer program stored thereon; A processor configured to execute the computer program in the memory to implement the steps of the method for processing gene data according to the present disclosure.

[0015] According to the above technical solution, the number of mutation genes associated with the target disease is determined to determine the probability of the target disease, and the greater the number of mutation genes associated with the target disease, the higher the probability of the target disease. The probability of the target disease can help users understand the intuitive disease condition, thereby assisting users to understand their physical condition.

[0016] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings are included to provide a further understanding of the present disclosure and constitute a part of the specification, which together with the following detailed description, serve to explain the present disclosure. In the drawings: Figure 1 is a step flow chart of a method for processing gene data according to an exemplary embodiment.

[0018] Figure 2 is a relationship diagram between a mutation gene and a target disease according to an exemplary embodiment.

[0019] Figure 3 is a step flow chart of a method for processing gene data according to an exemplary embodiment.

[0020] Figure 4 is a step flow chart of a method for processing gene data according to an exemplary embodiment.

[0021] Figure 5 is a schematic diagram of a first interface according to an exemplary embodiment.

[0022] Figure 6 is a schematic diagram of a first interface and a second interface according to an exemplary embodiment.

[0023] Figure 7 is a schematic diagram of a first interface and a second interface according to an exemplary embodiment.

[0024] Figure 8 is a step flow chart of a method for processing gene data according to an exemplary embodiment.

[0025] Figure 9 is a schematic diagram of a first interface and a third interface according to an exemplary embodiment.

[0026] Figure 10 is a schematic diagram of displaying diagnosis and treatment information in a first interface and a third interface according to an example embodiment.

[0027] Figure 11 is a step flow chart of a gene data processing method according to an example embodiment.

[0028] Figure 12 is a step flow chart of a gene data processing method according to an example embodiment.

[0029] Figure 13 is a schematic diagram of displaying reexamination detection methods in a first interface and a fifth interface according to an example embodiment.

[0030] Figure 14 is a schematic diagram of displaying reexamination detection methods in a first interface and a fifth interface according to an example embodiment.

[0031] Figure 15 is a step flow chart of a gene data processing method according to an example embodiment.

[0032] Figure 16 is a step flow chart of a gene data processing method according to an example embodiment.

[0033] Figure 17 is a block diagram of an electronic device according to an example embodiment.

[0034] Figure 18 is a block diagram of an electronic device according to an example embodiment. DETAILED DESCRIPTION

[0035] The specific embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely intended to illustrate and explain the present disclosure, and are not intended to limit the present disclosure.

[0036] Figure 1 is a gene data processing method according to an example embodiment, which is executed by an electronic device, which can be a medical device, a mobile phone, a computer, a tablet, or the like, and includes the following steps: In step S10, a mutation gene in a gene detection result of a user is identified.

[0037] The gene detection result is a detection result for displaying gene data of a user, which displays detected gene variations, expression levels, and chromosomal abnormalities, and includes gene sequence information, gene expression levels, chromosomal abnormality information, and the like.

[0038] For the gene sequence information, the gene sequence information includes genes, positions of the genes, variation frequencies of the genes, and the like. The genes are, for example, single nucleotide variations (SNVs), insertions / deletions (InDels), copy number variations (CNVs), and the like; the positions of the genes indicate gene names of the genes, chromosomal positions of the genes, and specific base change information, and the like; and the variation frequencies of the genes indicate abundances of the genes in the entire gene sample.

[0039] For the gene expression level, the gene expression level indicates a case of enhancement or weakening of transcription activity of a gene, and indicates the enhancement or weakening of the transcription activity by the number of messenger RNAs (mRNAs) transcribed from the gene in a cell. For example, a gene expression level of a gene transcribed into a messenger RNA is higher than normal, indicating that the expression activity of the gene is enhanced; and a gene expression level of a gene transcribed into a messenger RNA is lower than normal, indicating that the expression activity of the gene is weakened.

[0040] The change in the gene expression level is closely related to a functional state of a cell, occurrence and development of a disease, and a drug response, and the like. The gene expression level can be detected by RNA sequencing (RNA-seq), which can analyze types and quantities of messenger RNAs in a user's cell, thereby obtaining the gene expression level.

[0041] For the chromosomal abnormality information, the chromosomal abnormality information includes changes in the number of chromosomes, chromosomal structural abnormalities, and the like.

[0042] Optionally, in a case where at least one of the following phenomena occurs in a gene, the gene can be used as a mutant gene in the gene detection result: a gene mutation, a gene variation, a change in gene transcription activity, a change in the number of chromosomes, a change in the position of a chromosome, and the like.

[0043] In identifying the mutant gene in the gene detection result, a direct sequencing method, a PCR (Polymerase Chain Reaction) detection method, and the like can be used to identify the mutant gene in the gene detection result.

[0044] The direct sequencing method includes a Sanger (dideoxy chain termination method / first-generation sequencing) sequencing method and an NGS (Next Generation Sequencing) sequencing method, and the like. For example, for the Sanger sequencing method, labeled dideoxynucleotides (ddNTPs) are introduced in a DNA synthesis process to generate DNA fragments of different lengths, thereby determining a mutant gene in a DNA sequence; and for the NGS sequencing method, a high-throughput sequencing technology is used to detect mutations of a large number of genes, and is suitable for analyzing point mutations, insertions / deletions (indels), and the like in genes.

[0045] The PCR detection method includes an ARMS-PCR (amplification refractory mutation system PCR) method and a digital PCR (dPCR) method. For the ARMS-PCR method, for example, the 3' end of the primer is complementary to the mutation site through specific primers, and only the matched primer can perform the extension reaction, so as to detect the mutant gene; for the digital PCR, the sample is micro-dropletized, so that the reaction unit contains at most one target DNA molecule, and the absolute quantitative detection of the mutant gene is realized through the statistical analysis of the fluorescence signal.

[0046] Of course, in addition to the above method, other detection methods can also be used to identify the mutant gene in the gene detection result, and the present disclosure does not limit this.

[0047] In step S20, the target disease associated with the mutant gene is determined.

[0048] The target disease associated with the mutant gene is at least one, and the association of the mutant gene with the target disease means that the mutant gene can cause the user to have the target disease, and can also be understood as that the mutant gene is one of the reasons for the occurrence of the target disease. The target disease promoted by the mutant gene can be one disease or multiple diseases.

[0049] For example, taking the mutant gene TP53 as an example, the TP53 gene is a tumor suppressor gene, and the mutation of the TP53 gene can cause the cell to lose control of multiplication, thereby promoting the occurrence of lung cancer, breast cancer, rectal cancer, and ovarian cancer.

[0050] For example, taking the mutant gene APC as an example, the mutation of the APC gene can cause the cell to lose control of multiplication, thereby promoting the occurrence of colon cancer, rectal cancer, and other cancers.

[0051] The target disease associated with the mutant gene is at least one. The occurrence of the target disease can not be caused by one mutant gene, but can also be caused by multiple mutant genes.

[0052] For example, taking breast cancer as an example, the occurrence of breast cancer can be caused by the combined mutation of the TP53 gene, the PIK3CA gene, the HER2 gene, and other genes, or can be caused by the mutation of the TP53 gene.

[0053] In step S30, the probability of the occurrence of the target disease is determined according to the number of mutant genes associated with the target disease.

[0054] The number of mutation genes associated with the target disease is positively correlated with the probability of the target disease, and the higher the number of mutation genes associated with the target disease, the higher the probability of the user having the target disease. Of course, the number of mutation genes associated with the target disease can also be positively correlated with the severity of the target disease, and the more mutation genes, the higher the severity of the target disease.

[0055] In some scenarios, please refer to Figure 2 As shown, the mutation genes in the gene detection result have mutation gene A, mutation gene B and mutation gene C, mutation gene A is associated with target disease 1 and target disease 2, mutation gene B is associated with target disease 2 and target disease 3, and mutation gene C is associated with target disease 2, target disease 4 and target disease 5. The number of mutation genes associated with target disease 1, target disease 2, target disease 3, target disease 4 and target disease 5 is 1, 3, 1, 1 and 1 respectively. It can be seen that the user has the highest probability of having target disease 2, and has a lower probability of having target disease 1, 3, 4 and 5, and the probabilities of these target diseases are the same. Based on the intuitive probability of the target disease, the user can intuitively know their own physical condition, and the readability of the gene detection result is improved.

[0056] Through the above technical solution, the probability of the target disease can be determined according to the number of mutation genes associated with the target disease. The larger the number of mutation genes associated with the target disease, the higher the probability of the target disease. The probability of the target disease can help the user understand the intuitive disease condition, thereby assisting the user to understand their own physical condition, and improving the readability of the gene detection result.

[0057] Figure 3 The above step S20 involves an exemplary embodiment for interpreting the exemplary scheme for determining the target disease associated with the mutation gene, including the following steps: In step S21, the correlation between the mutation gene and the plurality of diseases is determined.

[0058] The correlation between the mutation gene and the disease indicates the importance of the presence of the mutation gene in causing the disease. The higher the correlation between the mutation gene and the disease, the more important the presence of the mutation gene in causing the disease; on the contrary, the lower the correlation between the mutation gene and the disease, the less important the presence of the mutation gene in causing the disease.

[0059] For example, taking breast cancer as an example, the occurrence of breast cancer can be caused by the combined mutation of TP53 gene, PIK3CA gene, HER2 gene and the like, but the important reason for promoting the occurrence of breast cancer is the presence of TP53 gene, and the importance of PIK3CA gene and HER2 gene decreases in turn, so the correlation between TP53 gene, PIK3CA gene, HER2 gene and breast cancer decreases gradually.

[0060] The historical gene data of a plurality of users who have a historical disease can be obtained from the database, the occurrence frequency of each historical mutation gene of each user who has the historical disease is counted according to the historical gene data, and the correlation between the historical disease and each historical mutation gene is obtained according to the occurrence frequency of each historical mutation gene; the same historical disease as the current disease is queried from the database, and the same historical mutation gene as the current mutation gene is queried, and the correlation between the queried historical disease and historical mutation gene is taken as the correlation between the current disease and the current mutation gene. Wherein, the more the occurrence frequency of the historical mutation gene, the higher the correlation between the historical mutation gene and the historical disease.

[0061] For example, taking breast cancer as an example, according to the historical gene data of 10,000 users with breast cancer, the occurrence frequency of TP53 gene mutation in 10,000 users with breast cancer is 9000, the occurrence frequency of PIK3CA gene mutation is 800, and the occurrence frequency of HER2 gene mutation is 50, so it can be determined that the correlation between TP53 gene, PIK3CA gene and HER2 gene and breast cancer is 90%, 8% and 0.5% respectively; if the current mutation gene of the user includes TP53 gene, the correlation between the TP53 gene and breast cancer is 90%.

[0062] For example, taking breast cancer as an example, according to the historical gene data of 10,000 users with breast cancer, the occurrence frequency of TP53 gene mutation in 10,000 users with breast cancer is 9000, the occurrence frequency of PIK3CA gene mutation is 800, and the occurrence frequency of HER2 gene mutation is 50, so it can be determined that the correlation between TP53 gene, PIK3CA gene and HER2 gene and breast cancer is 90%, 8% and 0.5% respectively; if the current mutation gene of the user includes TP53 gene, the correlation between the TP53 gene and breast cancer is 90%.

[0063] In step S22, a target disease with a correlation greater than a preset correlation with the mutation gene is selected from the plurality of diseases.

[0064] The correlation between the mutation gene and the disease is greater than the preset correlation, indicating that the mutation gene of the user is more likely to cause the user to have the disease, thereby identifying a mutation gene that is truly clinically significant for the disease and eliminating the remaining invalid mutation genes.

[0065] Continuing the example in step S21, taking 50% as the preset correlation, the correlation between the TP53 gene and breast cancer and gastric cancer is 90% and 70% respectively, both of which are greater than 50%, so the target diseases related to the TP53 gene include breast cancer and gastric cancer.

[0066] Through the above technical solution, the correlation between each mutation gene and each disease can be determined, and for each mutation gene, the target disease with strong correlation of the mutation gene is selected from a plurality of diseases, thereby associating the mutation gene with strong correlation with the target disease, and eliminating the influence of the mutation gene with low importance and low correlation for the target disease.

[0067] Figure 4 is an example embodiment related to the present disclosure, which is used to interpret the intuitive display of the disease probability effect diagram, including the following steps: In step S40, in response to a first trigger operation applied, a first interface is displayed.

[0068] The first trigger operation is an interactive operation of the user on the application. The first trigger operation can be a click operation, such as an operation triggered by the user clicking some entry, button or control on the application through a mouse. The first operation can also be a touch operation, such as a touch, swipe, click, long press, short press, etc. operation performed by the user's finger. The first operation can also be a keyboard input operation, such as an operation triggered by the user inputting text, shortcut keys or commands on the keyboard. The first operation can also be a voice instruction, such as an operation triggered by the user through voice. The first operation can also be a gesture operation, such as an operation triggered by the user's hand waving or the user's head nodding.

[0069] The first trigger operation can also be a time-based operation, such as a timed trigger or a periodic automatic trigger operation.

[0070] The first trigger operation can also be a condition-based trigger operation, which is triggered when a specific condition is met or specific data is received, such as an automatic trigger operation after receiving the user's disease probability of having each target disease.

[0071] The application can be an application installed on an electronic device, which can guide the user to participate through some interactive interface, provide flexible operation mode and interact with the user.

[0072] The first trigger operation is used to trigger the display of the first interface. The first trigger operation performed to display the first interface can be one or more, and this disclosure does not limit this. Of course, the subsequent triggering examples of the second to fifth triggering operations are also similar to the exemplary examples of the first triggering operation, and will not be repeated hereafter.

[0073] The first interface includes multiple object launch portals, and the objects can be preset functions of the application. For example, see Figure 5 The first interface shown in the application has at least one preset function such as symptom query function, diagnosis and treatment query function, comparison query function and review query function. The first interface includes a startup entrance corresponding to at least one object such as a symptom query entrance corresponding to the disease query function, a diagnosis and treatment query entrance corresponding to the diagnosis and treatment query function, a comparison query entrance of the comparison query function, and a review query entrance of the review query function.

[0074] Optionally, after obtaining the disease probabilities of the target diseases that the user suffers from, the target diseases may be arranged in descending order according to the disease probabilities.

[0075] Optionally, after obtaining the disease probabilities of the target diseases that the user suffers from, the target diseases whose disease probabilities are greater than the target probabilities may also be displayed.

[0076] In step S51, in response to a second triggering operation on the symptom query portal, a second interface is displayed.

[0077] The second trigger operation is an operation for displaying the second interface. The disease query entry triggered by the second trigger operation is used to link to the second interface. The disease query function is used to query the probability of the target disease suffered by the user. The disease query entry is the starting entry for querying the probability of disease.

[0078] The second interface is used to display a disease probability effect graph, which indicates a plurality of target diseases arranged in descending order of disease probability.

[0079] The disease probability effect graph can be a bar graph, pie chart, line graph, etc. Take the bar graph as an example, please refer to Figure 6 As shown, multiple target diseases can be arranged in descending order of disease probability; taking a pie chart as an example, see Figure 7 As shown, multiple target diseases can be divided according to the probability of disease, and the target disease with a higher disease probability occupies a higher proportion in the pie chart.

[0080] In addition, different colors can be used to distinguish when displaying the disease probability effect diagram. For example, target diseases with high disease probability are displayed in red or orange, and target diseases with low disease probability are displayed in blue or green.

[0081] Through the technical solution, in response to the second triggering operation on the disease query entry, the second interface can be displayed, and a disease probability effect diagram can be displayed on the second interface. The disease probability effect diagram can assist users, such as assisting the elderly, to quickly and intuitively understand the genetic test results, determine their own physical conditions, and bring convenience to users.

[0082] Figure 8 is an example embodiment related to the present disclosure, which is used to interpret the example scheme for querying the diagnosis and treatment information of each target disease, including the following steps: In step S52, in response to a third triggering operation on the diagnosis and treatment query entry corresponding to the target disease, a third interface is displayed.

[0083] The third triggering operation is an operation for displaying the third interface. The diagnosis and treatment query entry triggered by the third triggering operation is used to link the third interface. The application function corresponding to the diagnosis and treatment query entry is a diagnosis and treatment query function. The diagnosis and treatment query function is used to query diagnosis and treatment information related to the target disease. The diagnosis and treatment query entry is a starting entry for querying diagnosis and treatment information related to the target disease.

[0084] The diagnosis and treatment query entry can be displayed on the first interface as shown in Figure 9 , or can be displayed on the second interface as shown in Figure 10 . The present disclosure does not limit this.

[0085] Optionally, when the diagnosis and treatment query entry is displayed on the first interface as shown in Figure 9 , the diagnosis and treatment query entry is an entry for querying diagnosis and treatment information of multiple target diseases. In response to a third triggering operation on the diagnosis and treatment query entry, a third interface can be displayed. The third interface includes diagnosis and treatment information corresponding to each target disease. The diagnosis and treatment information includes at least one of a treatment plan and a disease cause of each target disease.

[0086] For example, in response to a third triggering operation of the user on the diagnosis and treatment query entry, a third interface is displayed. The third interface includes diagnosis and treatment information XXX, diagnosis and treatment information YYY, and diagnosis and treatment information ZZZ corresponding to each target disease such as gastric cancer, breast cancer, and lung cancer.

[0087] Optionally, when the diagnosis and treatment query entry is displayed on the second interface as shown in Figure 10 , the diagnosis and treatment query entry is an entry for querying diagnosis and treatment information of a certain target disease. In response to a third triggering operation on the diagnosis and treatment query entry corresponding to the target disease, a third interface is displayed. The third interface includes diagnosis and treatment information corresponding to the target disease. The diagnosis and treatment information includes at least one of a treatment plan and a disease cause of the target disease.

[0088] For example, the second interface displays a disease probability effect diagram, and in response to a third triggering operation of a user on a diagnosis and treatment query entry corresponding to a target disease in the disease probability effect diagram, a third interface is displayed, and the third interface includes diagnosis and treatment information corresponding to the target disease. For example, refer to Figure 10 As shown, if the disease probability effect diagram is a pie chart, each sector in the pie chart corresponds to a diagnosis and treatment query entry of a target disease. If the user clicks one of the sectors in the pie chart, the user can see the diagnosis and treatment information of the target disease in the third interface that is popped up.

[0089] Through the above technical solution, in response to a third triggering operation on a diagnosis and treatment query entry, a third interface is displayed, and the third interface can display at least one of a treatment plan and a disease cause corresponding to a target disease. By displaying the treatment plan and the disease cause corresponding to the target disease, the user can understand the treatment plan and the disease cause corresponding to the target disease, thereby facilitating the user to adaptively adjust the life and work according to the treatment plan and the disease cause.

[0090] In addition, when the second interface is displayed, in response to a third operation of a user on a diagnosis and treatment query entry of a target disease corresponding to a target disease on the second interface, diagnosis and treatment information of the target disease is displayed on the third interface. This can facilitate the user to trigger the display of the diagnosis and treatment information of the target disease on the currently viewed second interface, without returning to the previous interface or the main interface to view the diagnosis and treatment information, thereby facilitating the user operation.

[0091] Figure 11 This is an example embodiment related to the present disclosure, which is an example scheme for interpreting the comparison results between multiple genetic detection reports. The steps include: In step S53, in response to a fourth triggering operation on the comparison query entry, a fourth interface is displayed.

[0092] The fourth triggering operation is an operation for displaying the fourth interface. The comparison query entry triggered by the fourth triggering operation is used to link the fourth interface. The application function corresponding to the comparison query entry includes a comparison query function, which is used to generate comparison results according to the disease probabilities of each target disease in multiple genetic detection reports. The comparison query entry is a starting entry for querying the comparison results.

[0093] The fourth interface is used to display the comparison results between multiple genetic detection reports. Each genetic detection report includes the disease probability of each target disease.

[0094] Optionally, if the user has multiple genetic detection reports, the disease probabilities of each target disease in the multiple genetic detection reports can be compared to obtain the comparison results between the multiple genetic detection reports.

[0095] The comparison result between the multiple genetic detection reports includes a change amount of a probability of a same target disease (for example, an increase amount of the probability of the target disease or a decrease amount of the probability of the target disease), an added target disease, a reduced target disease, and the like of the probability of the target disease in the genetic detection report at a later time point compared with the genetic detection report at an earlier time point.

[0096] For example, the multiple genetic detection reports include a genetic detection report A and a genetic detection report B, the probabilities of lung cancer, breast cancer, and gastric cancer in the genetic detection report A are 90%, 80%, and 30% respectively, and the probabilities of lung cancer, breast cancer, and rectal cancer in the genetic detection report B are 90%, 70%, and 60% respectively, and then the comparison result between the genetic detection report B and the genetic detection report A includes that the probability of breast cancer decreases by 10%, rectal cancer is added, and gastric cancer is reduced.

[0097] According to the technical solution, in response to the fourth triggering operation on the comparison query portal, the fourth interface can be displayed, and the comparison result between the multiple genetic detection reports can be displayed on the fourth interface, which can assist the user to more intuitively view the change of the user's physical condition.

[0098] Figure 12 is an example embodiment related to the present disclosure, which is an example solution of the review means for paraphrasing query suggestions, and includes the following steps: In step S54, in response to a fifth triggering operation on the review query portal, a fifth interface is displayed.

[0099] The fifth triggering operation is an operation for displaying the fifth interface, and the review query portal triggered by the fifth triggering operation is used to link the fifth interface. The application function corresponding to the review query portal is a review query function, the review query function is used to recommend a review detection method according to the probability of a target disease, and the review query portal is a starting portal for querying the review detection method.

[0100] The fifth interface is used to display the review detection method of the target disease with a probability greater than a preset probability. When the probability of the target disease is greater than the preset probability, it means that the probability of the user suffering from the target disease is relatively large, and the biological feature sample of the user may be contaminated, or there may be medical instrument operation errors, etc., which may cause the probability of the target disease to be incorrect. Therefore, when the probability of the target disease is greater than the preset probability, it means that the probability of the user suffering from the target disease may be relatively large. In order to further ensure the accuracy of the detection and make the obtained probability more accurate, the fifth interface can be displayed, and the review detection method of the target disease is displayed on the fifth interface.

[0101] The review query portal can be displayed on the first interface as shown in Figure 13 , or can be displayed on the second interface as shown in Figure 14The second interface shown, and the present disclosure does not limit this.

[0102] Optionally, the review query entry is in Figure 13 When the first interface shown, the review query entry is a query entry for querying the review detection method of the target disease with a disease probability greater than a preset probability. In response to a third triggering operation on the review query entry, a fifth interface is displayed, and the fifth interface includes the review detection method of each target disease with a disease probability greater than the preset probability.

[0103] For example, in response to the fifth triggering operation of the user on the review query entry, the fifth interface includes the review detection method corresponding to each target disease with a disease probability greater than the preset probability, such as gastric cancer, breast cancer, lung cancer, etc.

[0104] Optionally, the review query entry is in Figure 14 When the second interface shown, the review query entry is a query entry for querying the review detection method of the target disease. In response to a fifth triggering operation on the diagnosis and treatment query entry corresponding to the target disease, a fifth interface is displayed, and the fifth interface includes the review detection method corresponding to the target disease.

[0105] For example, the second interface displays a disease probability effect diagram. In response to a fifth triggering operation of the user on the review query entry corresponding to the target disease in the disease probability effect diagram, a fifth interface is displayed, and the fifth interface includes the review detection method corresponding to the target disease. For example, if the disease probability effect diagram is a pie chart, each sector in the pie chart corresponds to a review query entry of the target disease. When the user clicks one of the sectors in the pie chart, the review detection method of the target disease can be seen in the fifth interface that pops up.

[0106] It can be understood that the fifth triggering operation is different from the third triggering operation described above. For example, the user can click the disease probability effect diagram in the second interface to pop up the diagnosis and treatment information of the target disease by a third triggering operation, and can pop up the review detection method of the target disease by a fifth triggering operation.

[0107] In some scenarios, the user can trigger the review query entry in the first interface or the second interface of the electronic device, and the electronic device pops up a fifth interface, and the fifth interface displays the review detection method corresponding to the target disease. The recommended review detection method includes but is not limited to recommended equipment, detection method, repeated detection, clinical detection, etc.

[0108] For example, the user obtains a high probability of suffering from thyroid cancer through gene detection, but the accuracy of the probability of suffering from the disease may not be high. After the user triggers the re-examination query portal, the re-examination detection method displayed in the fifth interface includes thyroid color Doppler ultrasound. The user can determine whether he has thyroid cancer by re-examination through thyroid color Doppler ultrasound.

[0109] Through the above technical solution, the fifth interface is displayed in response to the fifth triggering operation of the re-examination query portal. The fifth interface can display the re-examination detection method of the target disease with a disease probability greater than a preset probability, thereby recommending a reasonable re-examination detection method to the user when the user has a high probability of suffering from the target disease, to assist the user in understanding the detection means for re-examination of the target disease.

[0110] Moreover, when the second interface is displayed, the re-examination detection method of the target disease is displayed on the second interface in response to the fifth operation of the user on the re-examination query portal of the target disease corresponding to the target disease on the second interface. The user can trigger the display of the re-examination detection method of the target disease on the currently viewed second interface without returning to the previous interface or the main interface to view the re-examination detection method, thereby facilitating user operation.

[0111] Figure 15 An exemplary embodiment related to the present disclosure is shown for paraphrasing the genetic detection report after obtaining the genetic detection report, including the following steps: In step S61, the genetic detection report is broadcast.

[0112] The genetic detection report is generated based on the probability of suffering from the target disease. The genetic detection report includes the probability of suffering from each target disease.

[0113] The genetic detection report can be broadcast in the form of voice, text, video, etc. For example, the second interface can be displayed in response to the second triggering operation of the user on the disease query portal. The probability of suffering from each disease in the genetic detection report is broadcast in the form of text, voice, video, etc. on the second interface.

[0114] In step S71, the user's emotion is monitored during the broadcast of the genetic detection report.

[0115] During the broadcast of the genetic detection report, at least one of face recognition, voice recognition, heart rate recognition, etc. can be used to identify the facial emotion, voice emotion, and heart rate of the user. The user's emotion is obtained according to at least one of the facial emotion, voice emotion, and heart rate.

[0116] For example, when the user's facial emotion is anxiety and the heart rate is greater than a preset heart rate, it can be determined that the user's emotion is anxiety.

[0117] In step S81, in a case where it is identified that the user emotion is a first preset emotion, an emotion soothing strategy corresponding to the first preset emotion is executed.

[0118] The emotion soothing strategy corresponding to the first preset emotion is user soothing for a user having the first preset emotion, and the emotion soothing strategy includes but is not limited to music, dialogue, fragrance, video, etc.

[0119] Optionally, the emotion soothing strategies corresponding to different first preset emotions are different, and at least one of the emotion soothing manners such as music, dialogue, fragrance, and video can be used to soothe the user emotion.

[0120] For example, in a case where the user emotion is anxiety and the heart rate is greater than a preset heart rate, it can be determined that the user emotion is an anxiety emotion, and at this time, soothing music and fragrance can be played to soothe the anxiety emotion of the user.

[0121] For example, in a case where the user emotion is sadness and the heart rate is greater than a preset heart rate, it can be determined that the user emotion is a sadness emotion, and at this time, a dialogue with the user can be started to divert the user emotion.

[0122] Through the above technical solution, in the process of reporting the genetic detection report, the user emotion can be detected, and in a case where it is identified that the user emotion is a first preset emotion, an emotion soothing strategy corresponding to the first preset emotion is taken to soothe the user emotion, thereby assisting the user to complete the reporting and interpretation of the genetic detection report and improving the user's physical examination experience.

[0123] Figure 16 is an exemplary embodiment related to the present disclosure, which is used to interpret a to-be-handled disease condition determined by a user in the process of reporting a genetic detection report, and includes the following steps: In step S62, the genetic detection report is reported.

[0124] This step can refer to the exemplary embodiment of the above step S61, and will not be described here again.

[0125] In step S72, in the process of reporting the genetic detection report, the user emotion is monitored.

[0126] This step can refer to the exemplary embodiment of the above step S62, and will not be described here again.

[0127] In step S82, according to the identified user emotion, a to-be-handled disease condition in a plurality of target disease conditions is determined.

[0128] The to-be-handled disease condition is a target disease condition reported in a case where it is identified that the user emotion is a second preset emotion. When the user emotion is in the second preset emotion, it indicates that the user pays attention to the currently reported target disease condition.

[0129] For example, when it is identified that at least one of the following conditions is met: the facial emotion of the user in the user emotion is shock or fear, the tone and the speech speed in the user voice are accelerated, and the heart rate of the user is greater than a preset heart rate, the target disease currently announced by the electronic device can be determined as a to-be-handled disease, which is the target disease of interest of the user, and the professional personnel can explain the target disease to the user in a targeted manner.

[0130] By the above technical solution, the user emotion can be detected during the announcement of the genetic detection report, and the target emotion currently announced can be recorded when it is identified that the user emotion is the second preset emotion, so as to assist the professional personnel in further interpretation for the user according to the announced target emotion, and improve the physical examination experience of the user.

[0131] The following is an example embodiment related to the above step S30, which is used to explain an example scheme for correcting the disease probability according to the user age to obtain an accurate disease probability, including the following steps: In step S31, a first disease probability of the target disease is determined according to the number of mutation genes associated with the target disease.

[0132] The higher the number of mutation genes associated with the target disease, the higher the first disease probability of the user suffering from the target disease, which is a preliminary disease probability obtained based on the number of mutation genes.

[0133] In step S32, the first disease probability is corrected according to the user age to obtain a second disease probability.

[0134] For some target diseases, the larger the user age, the greater the probability of suffering from the target disease, and the smaller the user age, the smaller the probability of suffering from the target disease, so the first disease probability can be further adaptively corrected according to the user age to obtain a final second disease probability.

[0135] Optionally, the average age of suffering from the target disease can be determined first, if the current user age is greater than the average age, the first disease probability of the target disease can be corrected upward by a first preset value to obtain the second disease probability, and if the current user age is less than the average age, the first disease probability of the target disease can be corrected downward by a second preset value to obtain the second disease probability.

[0136] For example, the first preset value is 10%, the second preset value is also 10%, the average age is 40 years old, and the first probability of getting diabetes at 40 years old is 70%. If the current user age is 70 years old, the first probability of getting diabetes can be corrected upward by 10% to 80%; if the current user age is 10 years old, the first probability of getting diabetes can be corrected downward by 10% to 60%. In this way, the second probability of getting diabetes obtained can be adaptively adjusted according to the user age, and a more accurate second probability of getting diabetes is obtained.

[0137] Optionally, the first preset value or the second preset value can also be adjusted according to the difference between the current user age and the average age. The greater the difference between the current user age and the average age, the greater the first preset value and the second preset value are adjusted.

[0138] For example, the average age is 40 years old, and the first probability of getting diabetes at 40 years old is 70%. If the current user age is 50 years old, the difference between the current user age and the average age is 10, and the first preset value is 10%, the first probability of getting diabetes can be corrected upward by 10% to 80%; if the current user age is 60 years old, the difference between the current user age and the average age is 20, and the first preset value can be increased from 10% to 20%, and the first probability of getting diabetes can be corrected upward to 90%.

[0139] Through the above technical solutions, the first probability of getting diabetes obtained initially can be corrected according to the user age to obtain a more accurate second probability of getting diabetes, thereby providing a more accurate probability of getting diabetes for the user.

[0140] Figure 17 is a block diagram of an electronic device 1700 according to an example embodiment. As shown, the electronic device 1700 can include a processor 1701 and a memory 1702. The electronic device 1700 can also include one or more of a multimedia component 1703, an input / output (I / O) interface 1704, and a communication component 1705. The electronic device can be a medical device, a mobile phone, a computer, a tablet, or the like, which is not limited in the present disclosure. Figure 17

[0141] ​The processor 1701 is configured to control overall operations of the electronic device 1700 to complete all or part of the steps of the above-described genetic data processing method. The memory 1702 is configured to store various types of data to support operations of the electronic device 1700, which can include, for example, instructions for operating any application or method on the electronic device 1700, and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and the like. The memory 1702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk, or an optical disk. The multimedia component 1703 can include a screen and an audio component. The screen can be, for example, a touch screen, and the audio component is configured to output and / or input audio signals. For example, the audio component can include a microphone configured to receive external audio signals. The received audio signals can be further stored in the memory 1702 or transmitted through the communication component 1705. The audio component also includes at least one speaker configured to output audio signals. The I / O interface 1704 provides an interface between the processor 1701 and other interface modules, which can be a keyboard, a mouse, a button, and the like. The buttons can be virtual buttons or physical buttons. The communication component 1705 is configured to perform wired or wireless communication between the electronic device 1700 and other devices. The wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so the corresponding communication component 1705 can include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0142] In an exemplary embodiment, the electronic device 1700 can be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for performing the above-described method of processing genetic data.

[0143] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-described method of processing genetic data. For example, the computer-readable storage medium can be the above-described memory 1702 including program instructions, which can be executed by the processor 1701 of the electronic device 1700 to complete the above-described method of processing genetic data.

[0144] In another exemplary embodiment, a computer program product is also provided, which contains a computer program executable by a processor, which, when executed by the processor, implements the steps of the above-described method of processing genetic data.

[0145] Figure 18 is a block diagram of an electronic device 1800 according to an exemplary embodiment. For example, the electronic device 1800 can be provided as a server. Referring to Figure 18 , the electronic device 1800 includes a processor 1822, the number of which can be one or more, and a memory 1832 for storing a computer program executable by the processor 1822. The computer program stored in the memory 1832 can include one or more modules each corresponding to a set of instructions. In addition, the processor 1822 can be configured to execute the computer program to perform the above-described method of processing genetic data.

[0146] In addition, the electronic device 1800 can further include a power component 1826, which can be configured to perform power management of the electronic device 1800, and a communication component 1850, which can be configured to implement communication of the electronic device 1800, e.g., wired or wireless communication. In addition, the electronic device 1800 can further include an input / output (I / O) interface 1858. The electronic device 1800 can operate based on an operating system stored in the memory 1832.

[0147] In another exemplary embodiment, a computer readable storage medium including program instructions that, when executed by a processor, implement the steps of the method for processing genetic data described above is also provided. For example, the computer readable storage medium can be the memory 1832 described above including program instructions that are executable by the processor 1822 of the electronic device 1800 to complete the method for processing genetic data described above.

[0148] In another exemplary embodiment, a computer program product containing a computer program executable by a processor, which, when executed by the processor, implements the steps of the method for processing genetic data described above is also provided.

[0149] The preferred embodiments of the present disclosure are described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the specific details of the above-described embodiments. Various simple modifications can be made to the technical solutions of the present disclosure within the scope of the technical concept of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.

[0150] In addition, it should be noted that each specific technical feature described in the above specific embodiments can be combined in any appropriate manner without contradiction, and in order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0151] In addition, various different embodiments of the present disclosure can also be combined in any appropriate manner, as long as they do not deviate from the idea of the present disclosure, and they should also be considered as disclosed by the present disclosure.

Claims

1. A method for processing genetic data, characterized in that: include: Identify mutated genes in users’ genetic test results; determining a target disease condition associated with the mutated gene; There is at least one mutant gene associated with the target disease; The probability of suffering from the target disease is determined based on the number of mutant genes associated with the target disease; wherein the number of mutant genes associated with the target disease is positively correlated with the severity of the target disease.

2. The method according to claim 1, characterized in that Determining the target disease associated with the mutant gene comprises: determining the correlation between the mutant gene and multiple diseases; From the multiple diseases, a target disease having a correlation with the mutant gene greater than a preset correlation is screened out.

3. The method according to claim 1, characterized in that There are multiple target diseases; the method further comprises: In response to a first triggering operation on the application, a first interface is displayed; the first interface includes a symptom query entrance; In response to a second trigger operation on the disease query portal, a second interface is displayed; the second interface includes a disease probability effect diagram, and the disease probability effect diagram indicates multiple target diseases arranged from large to small disease probabilities.

4. The method according to claim 3, characterized in that The multiple target diseases each correspond to a diagnosis and treatment query entry; the method further includes: In response to a third trigger operation on the diagnosis and treatment query entry corresponding to the target disease, a third interface is displayed; the third interface is used to display diagnosis and treatment information, and the diagnosis and treatment information includes at least one of the treatment plan and cause of the target disease.

5. The method according to claim 3, characterized in that The first interface includes a comparison query entry; the method further includes: In response to a fourth trigger operation on the comparison query entry, a fourth interface is displayed; the fourth interface is used to display the comparison results between multiple gene test reports, and the gene test reports are generated based on the probability of suffering from the target disease.

6. The method according to claim 1, characterized in that The first interface includes a review query entry; the method further includes: In response to the fifth trigger operation on the review query entry, a fifth interface is displayed; the fifth interface is used to display the review detection method of the target disease whose probability of disease is greater than the preset probability.

7. The method according to claim 1, characterized in that The method further comprises: Broadcasting a genetic test report; the genetic test report is generated based on the probability of suffering from the target disease; monitoring the user's emotions during the broadcasting of the genetic test report; In a case where it is identified that the user emotion is a first preset emotion, an emotion soothing strategy corresponding to the first preset emotion is executed.

8. The method according to claim 1, characterized in that The method further comprises: Broadcasting a genetic test report; the genetic test report is generated based on the probability of suffering from the target disease; monitoring the user's emotions during the broadcasting of the genetic test report; According to the recognized user emotion, a to-be-treated symptom among a plurality of target symptoms is determined; the to-be-treated symptom is a target symptom reported when the user emotion is a second preset emotion.

9. The method according to claim 1, characterized in that Determining the probability of suffering from the target disease according to the number of mutant genes associated with the target disease includes: determining a first probability of disease occurrence of the target disease according to the number of mutated genes associated with the target disease; The first disease probability is corrected according to the user's age to obtain a second disease probability.

10. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 9.

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