Intelligent identification method and system for non-exosome particles in exosome separation

Abstract

The present invention relates to the field of artificial intelligence technology, and proposes a method and system for intelligently identifying non-exosome particles under exosome separation, including: generating a multidimensional parameter gradient through multidimensional separation parameters, using the multidimensional parameter gradient to separate exosome samples to obtain target exosomes; extracting multidimensional features of target exosomes, fusing the multidimensional features to obtain multidimensional fusion features of target exosomes; using a pre-constructed multidimensional decision tree recognition model to perform feature classification on the multidimensional fusion features to obtain a feature classification set of target exosomes; calculating the alienation value of the feature classification set through a preset alienation weight algorithm, and determining the target state of the target exosome according to the alienation value; generating an abnormal feedback matrix according to the target state and exosome category data, and identifying non-exosome particles in the target exosome according to the abnormal feedback matrix. The present invention can improve the accuracy of intelligent identification of non-exosome particles under exosome separation.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method and system for intelligently identifying non-exosome particles used in exosome separation. Background Art

[0002] Exosomes, as nanoparticles that carry cell information, play an important role in many physiological and pathological processes. Studying the composition and function of exosomes is of great significance for finding new diagnostic markers and therapeutic targets. However, in order to detect non-exosome particles in exosomes in a timely manner, it is necessary to accurately identify non-exosome particles separated from exosomes to identify abnormal exosomes.

[0003] Existing technologies for intelligent identification of non-exosome particles in exosome separation mostly use machine learning algorithms to identify and classify specific molecules in exosomes in order to identify abnormal exosomes. In practical applications, only considering identification and classification through a single feature may lead to overly one-sided identification of non-exosome particles, resulting in low accuracy of intelligent identification of non-exosome particles in exosome separation. Summary of the invention

[0004] The present invention provides a method and system for intelligently identifying non-exosome particles during exosome separation, the main purpose of which is to solve the problem of low accuracy in intelligently identifying non-exosome particles during exosome separation.

[0005] To achieve the above object, the present invention provides a method for intelligently identifying non-exosome particles in exosome separation, comprising:

[0006] S1. Obtain an exosome sample, generate a multidimensional parameter gradient using preset multidimensional separation parameters, and use the multidimensional parameter gradient to separate exosomes from the exosome sample to obtain target exosomes;

[0007] S2, extracting the multidimensional features of the target exosomes, and fusing the multidimensional features to obtain the multidimensional fusion features of the target exosomes;

[0008] S3, using a pre-built multidimensional decision tree recognition model to perform feature classification on the multidimensional fusion features to obtain a feature classification set of the target exosomes;

[0009] S4, calculating the alienation value of the feature classification set by a preset alienation weight algorithm, and determining the target state of the target exosome according to the alienation value;

[0010] S5. Generate an abnormal feedback matrix according to the target state and preset exosome category data, and identify non-exosome particles in the target exosomes according to the abnormal feedback matrix, wherein identifying non-exosome particles in the target exosomes according to the abnormal feedback matrix includes:

[0011] S51, calculating the target attribute value of the target exosome according to the feedback value and the state value in the abnormal feedback matrix:

[0012]

[0013] Where D is the target attribute value, f 1 is the feedback value, H is the state value, g 1 is the feedback threshold, g 2 is the state threshold;

[0014] S52: When the target attribute value is a preset target attribute threshold, determining the target exosome corresponding to the target attribute value as a non-exosome particle.

[0015] Optionally, generating a multidimensional parameter gradient by using preset multidimensional separation parameters comprises:

[0016] Extracting physical separation parameters and chemical separation parameters from preset multidimensional separation parameters;

[0017] Using the physical separation parameter and the chemical separation parameter as a first-level parameter gradient;

[0018] extracting a second-level parameter gradient in the chemical separation parameter;

[0019] A multi-dimensional parameter gradient is generated according to the first-level parameter gradient and the second-level parameter gradient.

[0020] Optionally, the using the multidimensional parameter gradient to separate exosomes from the exosome sample to obtain target exosomes comprises:

[0021] Generating a parameter separation order according to the multidimensional parameter gradient;

[0022] Separating the exosome samples one by one according to the parameter separation order to obtain a separated exosome set;

[0023] The isolated exosome collections are combined according to the same isolated exosomes to obtain target exosomes.

[0024] Optionally, extracting the multidimensional features of the target exosomes comprises:

[0025] extracting the protein content and nucleic acid content of the target exosomes;

[0026] Extracting the gene sequence and morphological structure corresponding to the target exosomes;

[0027] extracting surface markers of the target exosomes;

[0028] The protein content, the nucleic acid content, the gene sequence, the morphological structure and the surface marker are used as the multidimensional characteristics of the target exosomes.

[0029] Optionally, the step of fusing the multidimensional features to obtain the multidimensional fusion features of the target exosomes comprises:

[0030] Configuring initial feature weights for each feature in the multidimensional features one by one;

[0031] Adjust the initial feature weight according to the preset feature factor to obtain the feature optimization weight;

[0032] Sort each feature in the multidimensional feature according to the feature optimization weight to obtain a feature order, and generate a feature graph corresponding to the multidimensional feature according to the feature order;

[0033] A multidimensional fusion feature of the target exosome is generated according to the feature graph.

[0034] Optionally, the method of using a pre-built multidimensional decision tree recognition model to perform feature classification on the multidimensional fusion features to obtain a feature classification set of the target exosomes includes:

[0035] Performing feature quantization on the multi-dimensional fusion feature to obtain a multi-dimensional fusion quantized feature;

[0036] Mapping the decision tree branch type of each decision tree branch of the pre-constructed multidimensional decision tree recognition model according to the feature type in the multidimensional fusion quantitative feature;

[0037] According to the feature type and the decision tree branch type, the multidimensional fusion quantitative feature is compared with the classification threshold of each decision tree branch of the pre-constructed multidimensional decision tree recognition model to obtain a comparison factor;

[0038] Generating a comparison factor sequence of the multidimensional fusion feature according to the comparison factor;

[0039] Classifying the multidimensional fusion features into a normal feature set and an abnormal feature set by using the comparison factor sequence;

[0040] The normal feature set and the abnormal feature set are collected as a feature classification set.

[0041] Optionally, the calculating the alienation value of the feature classification set by using a preset alienation weight algorithm includes:

[0042] Extracting a feature weight corresponding to each feature in the feature classification set;

[0043] The alienation value of the feature classification set is calculated according to the feature weight and the comparison factor by using the following preset alienation weight algorithm:

[0044]

[0045] Wherein, P is the alienation value, w ij is the feature weight of the jth feature in the i-th alignment factor sequence, θ ij is the alignment factor of the jth feature in the ith alignment factor sequence, and n is the number of features.

[0046] Optionally, determining the target state of the target exosome according to the dissimilar value comprises:

[0047] The state value of the target exosome is calculated according to the dissimilation value, wherein the state value calculation formula is:

[0048]

[0049] Wherein, H is the state value, P is the alienation value, and δ is the feedback error value;

[0050] When the state value is a preset first target state threshold, the target state of the target exosome is a normal state;

[0051] When the state value is a preset second target state threshold, the target state of the target exosome is an abnormal state.

[0052] Optionally, generating an abnormal feedback matrix according to the target state and preset exosome category data includes:

[0053] Extracting exosome category features from preset exosome category data;

[0054] Comparing the multidimensional features of the target exosomes with the class features of the exosomes to obtain a feature feedback value;

[0055] An abnormal feedback matrix is ​​generated according to the target state and the characteristic feedback value.

[0056] In order to solve the above problems, the present invention also provides a non-exosome particle intelligent identification system for exosome separation, the system comprising:

[0057] An exosome separation module is used to obtain an exosome sample, generate a multidimensional parameter gradient through preset multidimensional separation parameters, and use the multidimensional parameter gradient to separate exosomes from the exosome sample to obtain target exosomes;

[0058] A feature fusion module, used to extract the multidimensional features of the target exosomes, and perform feature fusion on the multidimensional features to obtain the multidimensional fusion features of the target exosomes;

[0059] A multidimensional fusion feature classification module, used to classify the multidimensional fusion features using a pre-built multidimensional decision tree recognition model to obtain a feature classification set of the target exosomes;

[0060] A target state determination module, used to calculate the alienation value of the feature classification set by a preset alienation weight algorithm, and determine the target state of the target exosome according to the alienation value;

[0061] The non-exosome particle identification module is used to generate an abnormal feedback matrix according to the target state and preset exosome category data, and identify the non-exosome particles in the target exosomes according to the abnormal feedback matrix.

[0062] The embodiment of the present invention can separate exosome samples more effectively through multidimensional separation parameters and multidimensional parameter gradients, which is conducive to improving the purity and extraction efficiency of target exosomes; extracting multidimensional features of target exosomes and performing feature fusion is conducive to extracting key information from complex data, reducing feature dimensions, and improving the accuracy of subsequent classification; using a multidimensional decision tree recognition model to classify features, different types of exosomes can be distinguished, laying the foundation for subsequent abnormal detection; by calculating the alienation value of the feature classification set and determining the target state of the target exosome based on it, the situation of the exosomes can be more accurately judged, including whether there is an abnormal situation; generating an abnormal feedback matrix and identifying non-exosome particles, abnormal situations in exosome samples can be discovered early. Therefore, the method and system for intelligent identification of non-exosome particles under exosome separation proposed by the present invention can solve the problem of low accuracy of intelligent identification of non-exosome particles under exosome separation. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A schematic diagram of a process for intelligently identifying non-exosome particles in exosome separation provided by one embodiment of the present invention;

[0064] Figure 2 A schematic diagram of a process for generating a multi-dimensional parameter gradient according to an embodiment of the present invention;

[0065] Figure 3 A schematic diagram of a feature fusion process according to an embodiment of the present invention;

[0066] Figure 4 A functional module diagram of a non-exosome particle intelligent identification system for exosome separation provided by an embodiment of the present invention;

[0067] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0068] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0069] The embodiment of the present application provides a method for intelligent identification of non-exosome particles applied to exosome separation. The execution subject of the method for intelligent identification of non-exosome particles applied to exosome separation includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for intelligent identification of non-exosome particles applied to exosome separation can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0070] Reference Figure 1 As shown, it is a flow chart of a method for intelligently identifying non-exosome particles in exosome separation provided by one embodiment of the present invention. In this embodiment, the method for intelligently identifying non-exosome particles in exosome separation includes:

[0071] S1. Obtain an exosome sample, generate a multidimensional parameter gradient using preset multidimensional separation parameters, and use the multidimensional parameter gradient to separate exosomes from the exosome sample to obtain target exosomes.

[0072] In an embodiment of the present invention, the exosome sample refers to exosome sample data, that is, relevant information of bioactive molecules carried by exosomes, such as protein data, nucleic acid data, bioactive molecule function data, etc., wherein the uploaded exosome sample data can be stored in a storage area, and then the exosome sample data can be obtained from a pre-stored storage area through a computer statement with a data capture function (such as a Java statement, a Python statement, etc.), wherein the storage area includes but is not limited to a database and a blockchain.

[0073] Furthermore, in order to separate the exosome samples, multiple separation technologies need to be used for accurate separation. Therefore, it is necessary to comprehensively and accurately separate the exosome samples based on the separation parameters involved in the multiple separation technologies.

[0074] In an embodiment of the present invention, the multidimensional parameter gradient refers to arranging the parameters involved in the separation in a gradient form, and then separating the exosome sample according to the separation parameter sequence corresponding to the multidimensional parameter gradient.

[0075] In the embodiment of the present invention, refer to Figure 2 As shown, the generation of multidimensional parameter gradients by preset multidimensional separation parameters includes:

[0076] S21, extracting physical separation parameters and chemical separation parameters from the preset multidimensional separation parameters;

[0077] S22, using the physical separation parameter and the chemical separation parameter as a first-level parameter gradient;

[0078] S23, extracting the second level parameter gradient in the chemical separation parameter;

[0079] S24. Generate a multi-dimensional parameter gradient according to the first-level parameter gradient and the second-level parameter gradient.

[0080] In detail, physical separation parameters include but are not limited to ultrasound parameters and ultracentrifugation parameters, and chemical separation parameters include but are not limited to chromatography parameters, density parameters, and immunoaffinity parameters, wherein the physical separation parameters and chemical separation parameters can be custom configured or obtained from historical parameters, and then the physical separation parameters and chemical separation parameters are used as the first-level parameter gradient, such as the ultrasound parameters in the physical separation parameters as the first layer of the pyramid, the ultracentrifugation parameters as the second layer of the pyramid, the chromatography parameters as the third layer of the pyramid, the density parameters as the fourth layer of the pyramid, and the immunoaffinity parameters as the last layer of the pyramid.

[0081] Specifically, the chromatographic parameters in the chemical separation parameters can be divided according to the chromatographic range, and the density parameters can be divided according to the density size. Therefore, based on the chromatographic parameters as the third layer of the pyramid, the second-level parameter gradient is extended on the third layer, and the chromatographic range and density size are used as the second-level parameter gradient, and then a multidimensional parameter gradient is generated according to the gradient order of the first-level parameter gradient and the second-level parameter gradient.

[0082] Furthermore, the multidimensional parameter gradient can more accurately guide the separation process, which is beneficial to improve the separation efficiency of exosomes and reduce the mixing of impurities, thereby obtaining a purer target exosome sample.

[0083] In the embodiment of the present invention, the target exosomes refer to exosome molecules with higher purity separated from the exosome sample, that is, substances containing exosomes.

[0084] In an embodiment of the present invention, the method of separating exosomes from the exosome sample using the multidimensional parameter gradient to obtain target exosomes includes:

[0085] Generating a parameter separation order according to the multidimensional parameter gradient;

[0086] Separating the exosome samples one by one according to the parameter separation order to obtain a separated exosome set;

[0087] The isolated exosome collections are combined according to the same isolated exosomes to obtain target exosomes.

[0088] In detail, the parameter separation order is generated according to the parameter hierarchy order of the multidimensional parameter gradient. For example, the ultrasound parameters in the physical separation parameters in the multidimensional parameter gradient are used as the first layer of the pyramid, the ultracentrifugation parameters are used as the second layer of the pyramid, the chromatography parameters are used as the third layer of the pyramid, the density parameters are used as the fourth layer of the pyramid, and the immunoaffinity parameters are used as the last layer of the pyramid. The order of ultracentrifugation parameters, chromatography parameters, chromatography range, density parameters, density size, and immunoaffinity parameters is used as the parameter separation order, and then different separation parameters are applied to the exosome sample one by one according to the parameter separation order, so as to obtain a set of separated substances corresponding to each separation parameter, that is, a separated exosome set.

[0089] Specifically, all different substances in the isolated exosome set are deleted, and the same substances are merged. For example, the isolated exosome set A includes (A 1 ,A 2 ,A 3 ), the isolated exosome set B includes (B 1 ,A 2 ,B 3 ), the isolated exosome collection C includes (C 1 ,A 2 ,C 3 ), then the same substances are merged and the different substances are deleted, so only substance A exists. 2 , then A 2 Targeted exosomes.

[0090] Furthermore, in order to accurately identify abnormal exosome substances, it is necessary to comprehensively describe the characteristics of exosomes from different perspectives. Therefore, it is necessary to extract the multidimensional characteristics of target exosomes, which is conducive to a more comprehensive understanding of the properties and functions of exosomes.

[0091] S2. Extracting the multidimensional features of the target exosomes, and fusing the multidimensional features to obtain the multidimensional fusion features of the target exosomes.

[0092] In the embodiments of the present invention, the multidimensional features refer to multiple characteristics of the target exosomes, including but not limited to morphological characteristics, biological characteristics, and functional characteristics.

[0093] In an embodiment of the present invention, the step of extracting the multidimensional features of the target exosomes includes:

[0094] extracting the protein content and nucleic acid content of the target exosomes;

[0095] Extracting the gene sequence and morphological structure corresponding to the target exosomes;

[0096] extracting surface markers of the target exosomes;

[0097] The protein content, the nucleic acid content, the gene sequence, the morphological structure and the surface marker are used as the multidimensional characteristics of the target exosomes.

[0098] In detail, the protein content in the target exosomes can be determined by methods such as mass spectrometry or immunoblotting, that is, mass spectrometry can help identify the types and relative abundance of proteins in exosomes, while immunoblotting can be used to detect the presence and quantity of specific proteins; nucleic acids can be extracted from target exosomes using tools such as nucleic acid extraction kits, and the nucleic acid content can be determined by colorimetry, fluorescence or real-time fluorescence quantitative PCR; RNA sequencing technology can be used to obtain RNA sequence information in exosomes, and then gene sequence information including mRNA, miRNA, etc. can be derived; high-resolution microscopes such as electron microscopes and atomic force microscopes can be used to observe the morphological structure of target exosomes, including size, shape, surface structure and other information; flow cytometry, immunofluorescence staining and other technologies can be used to detect markers on the surface of exosomes, such as the expression of specific proteins or other molecules.

[0099] Furthermore, by taking protein content, nucleic acid content, gene sequence, morphological structure and surface markers as multidimensional features of exosomes and fusing them, we can obtain more comprehensive and diversified characteristic data describing the characteristics of exosomes. By extracting and fusing these multidimensional features, we can more comprehensively and accurately describe the characteristics of target exosomes, providing an important information basis for subsequent research and application.

[0100] In an embodiment of the present invention, the multidimensional fusion feature refers to the integration and fusion of multidimensional data features from different sources or different types to generate more comprehensive, accurate and useful features. By fusing these multidimensional data features together, information from all aspects can be integrated to make up for the limitations of a single feature and improve the characterization ability and discrimination of the feature.

[0101] In the embodiment of the present invention, refer to Figure 3 As shown, the multi-dimensional features are subjected to feature fusion to obtain the multi-dimensional fusion features of the target exosomes, including:

[0102] S31, configuring an initial feature weight for each feature in the multidimensional feature one by one;

[0103] S32, adjusting the initial feature weight according to the preset feature factor to obtain the feature optimization weight;

[0104] S33, sorting each feature in the multidimensional feature according to the feature optimization weight to obtain a feature order, and generating a feature graph corresponding to the multidimensional feature according to the feature order;

[0105] S34, generating a multi-dimensional fusion feature of the target exosome according to the feature graph.

[0106] In detail, for each multidimensional feature (protein content, nucleic acid content, gene sequence, morphological structure and surface marker), a custom initial weight is configured for it to reflect its importance in feature fusion. According to the preset characteristic factor, the initial weight of each feature is adjusted to obtain the feature optimization weight, ensuring that the contribution of each feature in the fusion meets the actual needs. The characteristic factor refers to the difficulty of extracting each feature. The weight of the easy-to-extract feature is large, and the weight of the difficult-to-extract feature is small. This can ensure that in the feature fusion process, the contribution of different features can be reasonably adjusted according to their difficulty, so as to better reflect the importance of each feature in the overall feature representation, so that the weight of each feature can be determined according to the real-time extraction difficulty, and then the initial weight of the feature is adjusted according to the characteristic factor to obtain the feature optimization weight.

[0107] Specifically, according to the feature optimization weight, each feature is sorted to determine the feature order, and the feature optimization weights are sorted in descending order to obtain the feature order, and then a circular feature map is generated according to the feature order. The feature map reflects the importance and contribution of each feature in the fusion. Finally, according to the generated feature map, the multidimensional features are fused in combination with the weights and order of each feature to obtain the multidimensional fusion features of the target exosomes. The multidimensional fusion features integrate the information of each feature to more comprehensively and accurately describe the characteristics of exosomes.

[0108] Furthermore, it is necessary to perform feature classification on the multidimensional fusion features. Therefore, the decision tree model can better process complex multidimensional feature data and improve the classification accuracy and precision of target exosomes.

[0109] S3. Using a pre-built multidimensional decision tree recognition model to perform feature classification on the multidimensional fusion features, to obtain a feature classification set of the target exosomes.

[0110] In an embodiment of the present invention, the pre-constructed multidimensional decision tree recognition model is determined according to the number of features in the multidimensional fusion features, that is, if there are 5 features in the multidimensional fusion features, the multidimensional decision tree recognition model is composed of 5 decision tree branches, each decision tree branch corresponds to a feature, and the feature category corresponding to a feature can be detected, and then the decision tree model is trained through historical feature data and continuously optimized until the optimization conditions are met, and the output decision tree model is a multidimensional decision tree recognition model.

[0111] In an embodiment of the present invention, the feature classification set refers to a result set obtained after classifying the multidimensional fusion features according to the multidimensional decision tree model, namely, a normal feature set and an abnormal feature set. The normal feature set contains feature samples classified as normal, and the abnormal feature set contains feature samples classified as abnormal.

[0112] In an embodiment of the present invention, the multidimensional fusion features are classified using a pre-built multidimensional decision tree recognition model to obtain a feature classification set of the target exosomes, including:

[0113] Performing feature quantization on the multi-dimensional fusion feature to obtain a multi-dimensional fusion quantized feature;

[0114] Mapping the decision tree branch type of each decision tree branch of the pre-constructed multidimensional decision tree recognition model according to the feature type in the multidimensional fusion quantitative feature;

[0115] According to the feature type and the decision tree branch type, the multidimensional fusion quantitative feature is compared with the classification threshold of each decision tree branch of the pre-constructed multidimensional decision tree recognition model to obtain a comparison factor;

[0116] Generating a comparison factor sequence of the multidimensional fusion feature according to the comparison factor;

[0117] Classifying the multidimensional fusion features into a normal feature set and an abnormal feature set by using the comparison factor sequence;

[0118] The normal feature set and the abnormal feature set are collected as a feature classification set.

[0119] In detail, the original multidimensional fusion features are processed and converted into numerical data. The multidimensional fusion features include protein content, nucleic acid content, gene sequence, morphological structure and surface markers. The protein content and nucleic acid content are directly used as numerical data. The gene sequence is usually a string composed of four bases (A, T, C, G), which can be converted into numerical data through encoding, such as using a single letter to represent each base and then mapping it to a number; the morphological structure can extract key features through feature extraction algorithms, and these features can be converted into numerical data, such as morphological parameters such as size and shape can be extracted and converted into numerical values; surface markers are usually the expression of proteins or other molecules, which can be quantified into numerical data to represent their relative expression level or quantity, thereby obtaining multidimensional fusion quantitative features.

[0120] Specifically, the feature types contained in the multidimensional fusion quantitative features are matched with the feature classifications corresponding to each branch in the multidimensional decision tree. For example, the multidimensional fusion quantitative features are {A, B, C, D, E}, and the classification features corresponding to each decision tree branch in the multidimensional decision tree are {A, B, C, D, E}. Then, the features are mapped to the decision tree branches according to the same feature types. Then, the multidimensional fusion quantitative features are compared with the classification thresholds of the corresponding decision tree branches to obtain the comparison results as the comparison factors. The comparison factors are configured as 1 for those greater than or equal to the classification threshold and as 0 for those less than the classification threshold. Then, it can be obtained whether the feature corresponds to a normal feature or an abnormal feature. The comparison factors are obtained based on each decision tree branch, and then a sequence is generated for the comparison factors obtained from all decision tree branches to represent the comparison of the features. Thus, according to the characteristics of the comparison factor sequence, the multidimensional fusion features are divided into two sets of normal features and abnormal features, and the feature sets classified as normal and abnormal are summarized as the final feature classification set of the target exosomes.

[0121] Exemplarily, the multidimensional fusion quantitative features are {A, B, C, D, E}, and the multidimensional fusion quantitative features are input into each decision tree branch in the multidimensional decision tree in turn. The result outputted by the decision tree branch corresponding to feature A is {1, 0, 0, 0, 0}, indicating that the value corresponding to feature A is greater than the classification threshold, and feature A is an abnormal feature. {1, 0, 0, 0, 0} represents the classification values ​​corresponding to {A, B, C, D, E} respectively, and in the decision tree branch corresponding to feature A, the classification values ​​corresponding to features B, C, D, and E are all 0; the result outputted by the decision tree branch corresponding to feature B is {0, 1, 0, 0, 0}, indicating that the value corresponding to feature B is greater than the classification threshold. value, feature B is an abnormal feature; the result of the decision tree branch output based on feature C is {0,0,0,0,0}, which means that the value corresponding to feature C is less than the classification threshold, then feature C is a normal feature, and so on. After all features are compared, contrast factors will be obtained, and then all contrast factors will generate contrast factor sequences, that is, {{1,0,0,0,0},{0,1,0,0,0},}0,0,0,0,0{,…,}, and then the contrast factors with a value of 1 greater than 1 in the contrast factors are taken as abnormal feature sets, and when the contrast factors with values ​​of 0 in the contrast factors are taken as normal feature sets, according to the contrast factor sequence For classification, the features {A, B} are the abnormal feature set, and the feature {C} is the normal feature set, and then {{A, B}, {C}} is taken as the final feature classification set of the target exosomes.

[0122] Furthermore, status evaluation of the feature classification set is helpful to quantify the importance of each feature to the overall classification result, analyze the impact of different feature combinations on the classification results, and discover the interaction rules between features.

[0123] S4. Calculate the alienation value of the feature classification set by using a preset alienation weight algorithm, and determine the target state of the target exosomes according to the alienation value.

[0124] In the embodiment of the present invention, the alienation value refers to the degree of change or difference of each feature in the feature classification set, that is, a quantitative indicator of the comprehensive influence of the feature classification set calculated based on the feature weight and the comparison factor.

[0125] In the embodiment of the present invention, the calculation of the alienation value of the feature classification set by using a preset alienation weight algorithm includes:

[0126] Extracting a feature weight corresponding to each feature in the feature classification set;

[0127] The alienation value of the feature classification set is calculated according to the feature weight and the comparison factor by using the following preset alienation weight algorithm:

[0128]

[0129] Wherein, P is the alienation value, w ij is the feature weight of the jth feature in the i-th alignment factor sequence, θ ij is the alignment factor of the jth feature in the ith alignment factor sequence, and n is the number of features.

[0130] In detail, when performing feature fusion in a feature classification set, each feature has a feature optimization weight, that is, the feature weight corresponding to each feature is extracted from the feature graph, and then the feature alienation value of the feature classification set is calculated according to the feature weight and the comparison factor. For example, for the feature classification set {{A,B},{C}}, the alienation value of {A,B} is first calculated, the comparison factor corresponding to feature A is {1,0,0,0,0}, the comparison factor corresponding to feature B is {0,1,0,0,0}, and the comparison factor corresponding to feature C is {0,0,0,0,0}. The alienation value is calculated according to the numerical value in the comparison factor sequence and the corresponding feature weight of each feature, such as the alienation value P of {C} is 0.

[0131] Specifically, the feature weight based on each feature in the alienation weight algorithm can quantify the importance of each feature to the overall classification result. By calculating the alienation value, the contribution of different features in the classification process can be obtained, which helps to understand the relative importance of features. The final P is the alienation value of the feature classification set, which reflects the comprehensive influence of the feature set under different comparison factors.

[0132] Furthermore, the dissimilar value can help quantify the degree of state change of the target exosomes under different conditions. By analyzing the dissimilar value, we can more accurately understand the changes in the characteristic combination of target exosomes under different conditions.

[0133] In the embodiment of the present invention, the target state includes the normal state or abnormal state of the target exosomes, which reflects the normal or abnormal condition of the entity under specific conditions.

[0134] In an embodiment of the present invention, determining the target state of the target exosome according to the dissimilation value includes:

[0135] The state value of the target exosome is calculated according to the dissimilation value, wherein the state value calculation formula is:

[0136]

[0137] Wherein, H is the state value, P is the alienation value, and δ is the feedback error value;

[0138] When the state value is a preset first target state threshold, the target state of the target exosome is a normal state;

[0139] When the state value is a preset second target state threshold, the target state of the target exosome is an abnormal state.

[0140] In detail, the state value of the target exosomes can be calculated according to the alienation value, that is, when the alienation value is equal to 0, the state value is determined by the feedback error value, and the feedback error value is added on the basis of the state value. The feedback error value can realize automatic adjustment and feedback control to ensure rapid response and stability under changing conditions. By analyzing the changing trend and size of the feedback error value, the control strategy can be optimized; and when the alienation value is not equal to 0, the feedback error value is added on the basis of the state value, so as to obtain a more accurate state value.

[0141] Specifically, the first target state threshold is a value of 0, and the second target state threshold is a value of 1. When there is no feedback error, the feedback error value is set to 0. When the target state value is a value of 0, the target state of the target exosome is determined to be a normal state; when the target state value is a value of 1, the target state of the target exosome is determined to be an abnormal state.

[0142] Furthermore, after determining the target state of the target exosomes, it is also necessary to accurately detect the state of the target exosomes based on the feedback, that is, by generating an abnormal feedback matrix, the target state of the exosomes can be compared with the actual state, thereby identifying possible abnormalities.

[0143] S5. Generate an abnormal feedback matrix according to the target state and preset exosome category data, and identify non-exosome particles in the target exosomes according to the abnormal feedback matrix.

[0144] In the embodiment of the present invention, the abnormal feedback matrix is ​​used to record the relationship between the target exosomes and the state value and the characteristic feedback value, and can identify whether the target exosomes meet the expected state and whether the target exosomes are abnormal.

[0145] In an embodiment of the present invention, generating an abnormal feedback matrix according to the target state and preset exosome category data includes:

[0146] Extracting exosome category features from preset exosome category data;

[0147] Comparing the multidimensional features of the target exosomes with the class features of the exosomes to obtain a feature feedback value;

[0148] An abnormal feedback matrix is ​​generated according to the target state and the characteristic feedback value.

[0149] In detail, the exosome category data includes normal exosome data and non-exosome particle data, and then the normal data features of the normal exosome data are extracted, and the abnormal data features of the non-exosome particle data are extracted. The normal data features include the protein content, nucleic acid content, gene sequence, morphological structure and surface marker features of the normal exosome sample, and the abnormal data features include the protein content, nucleic acid content, gene sequence, morphological structure and surface marker features of the non-exosome particle sample. Then, the multidimensional features of the target exosomes are compared and analyzed with the predetermined exosome category features one by one, and the difference or similarity between them is calculated to obtain a feature feedback value. The feature feedback value can indicate the degree of similarity or difference between the target exosome and the preset category data, so that according to the target state and the feature feedback value, these data can be integrated together to form an abnormal feedback matrix. In this matrix, each row represents a target exosome sample, and the column represents the exosome category feature and the corresponding feature feedback value. Through the matrix structure, the relationship between the target exosome and the preset category data can be clearly displayed, which is helpful for further analysis and identification of abnormal situations.

[0150] Furthermore, by analyzing the abnormal feedback matrix, abnormalities in the target exosomes can be detected early, and even minor changes can be captured, thereby identifying abnormal exosome samples.

[0151] In the embodiments of the present invention, the non-exosome particles refer to the situation where the quantity, composition or function of exosomes (a type of small vesicles released by cells) is abnormal, such as changes in the molecular composition carried by exosomes, for example, abnormal increases or decreases in the content of certain specific proteins or nucleic acids.

[0152] In an embodiment of the present invention, the identifying non-exosome particles in the target exosomes according to the abnormal feedback matrix includes:

[0153] The target attribute value of the target exosome is calculated according to the feedback value in the abnormal feedback matrix and the state value:

[0154]

[0155] Where D is the target attribute value, f 1 is the feedback value, H is the state value, g 1 is the feedback threshold, g 2 is the state threshold;

[0156] When the target attribute value is a preset target attribute threshold, the target exosome corresponding to the target attribute value is determined as a non-exosome particle.

[0157] In detail, when the feedback value in the abnormal feedback matrix is ​​equal to the preset feedback threshold and the state value is equal to the preset state threshold, its target attribute value is the difference between the feedback value and the state value; and when the feedback value in the abnormal feedback matrix is ​​not equal to the preset feedback threshold or the state value is not equal to the preset state threshold, its target attribute value is the sum of the difference between the feedback value and the state value and the value 1, and then by comparing the target attribute value D with the preset target attribute threshold, if the target attribute value D reaches the target attribute threshold, the target exosomes can be determined as non-exosome particles.

[0158] For example, when the feedback threshold g 1 =0, state threshold g 2 =0, that is, when the feedback value f 1 = 0 and the state value H = 0, the target attribute value D = 0, and when the feedback value f 1 ≠0 or the state value H≠0, the target attribute value D≥1, and the target attribute threshold is 0. When the target attribute value D reaches the target attribute threshold, the target exosomes can be determined as non-exosome particles. Based on the feedback value and state value in the abnormal feedback matrix, by setting the feedback threshold and state threshold, it is judged whether the target exosomes belong to non-exosome particles, which is conducive to the rapid identification of non-exosome particles and provides an important reference for further diagnosis and treatment.

[0159] The embodiment of the present invention can separate exosome samples more effectively through multidimensional separation parameters and multidimensional parameter gradients, which is conducive to improving the purity and extraction efficiency of target exosomes; extracting multidimensional features of target exosomes and performing feature fusion is conducive to extracting key information from complex data, reducing feature dimensions, and improving the accuracy of subsequent classification; using a multidimensional decision tree recognition model to classify features, different types of exosomes can be distinguished, laying the foundation for subsequent abnormal detection; by calculating the alienation value of the feature classification set and determining the target state of the target exosome based on it, the situation of the exosomes can be more accurately judged, including whether there is an abnormal situation; generating an abnormal feedback matrix and identifying non-exosome particles, abnormal situations in exosome samples can be discovered early. Therefore, the method and system for intelligent identification of non-exosome particles under exosome separation proposed by the present invention can solve the problem of low accuracy of intelligent identification of non-exosome particles under exosome separation.

[0160] like Figure 4 , is a functional module diagram of a non-exosome particle intelligent identification system for exosome separation provided by one embodiment of the present invention.

[0161] The non-exosome particle intelligent identification system 100 for exosome separation of the present invention can be installed in an electronic device. According to the functions to be implemented, the non-exosome particle intelligent identification system 100 for exosome separation may include an exosome separation module 101, a feature fusion module 102, a multi-dimensional fusion feature classification module 103, a target state determination module 104 and a non-exosome particle identification module 105. The module of the present invention may also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0162] In this embodiment, the functions of each module / unit are as follows:

[0163] The exosome separation module 101 is used to obtain an exosome sample, generate a multidimensional parameter gradient through a preset multidimensional separation parameter, and use the multidimensional parameter gradient to separate exosomes from the exosome sample to obtain target exosomes;

[0164] The feature fusion module 102 is used to extract the multidimensional features of the target exosomes, and perform feature fusion on the multidimensional features to obtain the multidimensional fusion features of the target exosomes;

[0165] The multidimensional fusion feature classification module 103 is used to classify the multidimensional fusion features using a pre-built multidimensional decision tree recognition model to obtain a feature classification set of the target exosomes;

[0166] The target state determination module 104 is used to calculate the alienation value of the feature classification set by using a preset alienation weight algorithm, and determine the target state of the target exosome according to the alienation value;

[0167] The non-exosome particle identification module 105 is used to generate an abnormal feedback matrix according to the target state and preset exosome category data, and identify the non-exosome particles in the target exosomes according to the abnormal feedback matrix.

[0168] In detail, each module described in the non-exosome particle intelligent identification system 100 for exosome separation described in the embodiment of the present invention is used in the same manner as described above. Figures 1 to 3 The same technical means as the method for intelligent identification of non-exosome particles in exosome separation described in the text can produce the same technical effects, so they will not be repeated here.

[0169] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0170] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0171] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0172] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0173] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is not limited only according to the above description, and it is intended that all changes within the meaning and scope of equivalent elements within the scope of protection are included in the present invention.

[0174] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0175] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units or systems stated in the system embodiment can also be implemented by one unit or system through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any specific order.

[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for intelligently identifying non-exosome particles in exosome separation, characterized in that: The method comprises: S1. Obtain an exosome sample, and generate a multidimensional parameter gradient using a preset multidimensional separation parameter, including: extracting a physical separation parameter and a chemical separation parameter from the preset multidimensional separation parameter; using the physical separation parameter and the chemical separation parameter as a first-level parameter gradient; extracting a second-level parameter gradient from the chemical separation parameter; generating a multidimensional parameter gradient based on the first-level parameter gradient and the second-level parameter gradient; and performing exosome separation on the exosome sample using the multidimensional parameter gradient to obtain target exosomes; S2, extracting the multidimensional features of the target exosomes, and fusing the multidimensional features to obtain the multidimensional fusion features of the target exosomes; S3, using a pre-built multidimensional decision tree recognition model to perform feature classification on the multidimensional fusion feature to obtain a feature classification set of the target exosomes, wherein the multidimensional decision tree recognition model is determined according to the number of features in the multidimensional fusion feature, each decision tree branch corresponds to a feature, and the feature category corresponding to each feature is detected, and the preset decision tree model is trained through historical feature data, and continuously optimized until the optimization conditions are met, and the output decision tree model is a multidimensional decision tree recognition model; S4, calculating the alienation value of the feature classification set by a preset alienation weight algorithm, and determining the target state of the target exosome according to the alienation value; S5. Generate an abnormal feedback matrix according to the target state and preset exosome category data, and identify non-exosome particles in the target exosomes according to the abnormal feedback matrix, wherein identifying non-exosome particles in the target exosomes according to the abnormal feedback matrix includes: S51, calculating the target attribute value of the target exosome according to the feedback value and the state value in the abnormal feedback matrix: in, is the target attribute value, is the feedback value, is the state value, is the feedback threshold, is the state threshold; S52: When the target attribute value is a preset target attribute threshold, determining the target exosome corresponding to the target attribute value as a non-exosome particle.

2. The method for intelligently identifying non-exosome particles in exosome separation according to claim 1, characterized in that: The method of separating exosomes from the exosome sample using the multidimensional parameter gradient to obtain target exosomes includes: Generating a parameter separation order according to the multidimensional parameter gradient; Separating the exosome samples one by one according to the parameter separation order to obtain a separated exosome set; The isolated exosome collections are combined according to the same isolated exosomes to obtain target exosomes.

3. The method for intelligently identifying non-exosome particles in exosome separation according to claim 1, characterized in that: The step of extracting the multidimensional features of the target exosomes comprises: extracting the protein content and nucleic acid content of the target exosomes; Extracting the gene sequence and morphological structure corresponding to the target exosomes; extracting surface markers of the target exosomes; The protein content, the nucleic acid content, the gene sequence, the morphological structure and the surface marker are used as the multidimensional characteristics of the target exosomes.

4. The method for intelligently identifying non-exosome particles in exosome separation according to claim 1, characterized in that: The step of fusing the multidimensional features to obtain the multidimensional fusion features of the target exosomes includes: Configuring initial feature weights for each feature in the multidimensional features one by one; Adjust the initial feature weight according to the preset feature factor to obtain the feature optimization weight; Sort each feature in the multidimensional feature according to the feature optimization weight to obtain a feature order, and generate a feature graph corresponding to the multidimensional feature according to the feature order; A multidimensional fusion feature of the target exosome is generated according to the feature graph.

5. The method for intelligently identifying non-exosome particles in exosome separation according to claim 1, characterized in that: The method of using a pre-built multidimensional decision tree recognition model to perform feature classification on the multidimensional fusion features to obtain a feature classification set of the target exosomes includes: Performing feature quantization on the multi-dimensional fusion feature to obtain a multi-dimensional fusion quantized feature; Mapping the decision tree branch type of each decision tree branch of the pre-constructed multidimensional decision tree recognition model according to the feature type in the multidimensional fusion quantitative feature; According to the feature type and the decision tree branch type, the multidimensional fusion quantitative feature is compared with the classification threshold of each decision tree branch of the pre-constructed multidimensional decision tree recognition model to obtain a comparison factor; Generating a contrast factor sequence of the multi-dimensional fusion feature according to the contrast factor; Classifying the multidimensional fusion features into a normal feature set and an abnormal feature set by using the contrast factor sequence; The normal feature set and the abnormal feature set are collected as a feature classification set.

6. The method for intelligently identifying non-exosome particles in exosome separation according to claim 5, characterized in that: The calculating the alienation value of the feature classification set by using a preset alienation weight algorithm includes: Extracting a feature weight corresponding to each feature in the feature classification set; The alienation value of the feature classification set is calculated according to the feature weight and the contrast factor by using the following preset alienation weight algorithm: in, is the alienation value, For the The first The feature weight of each feature, For the The first The contrast factor of the feature, is the number of features.

7. The method for intelligently identifying non-exosome particles in exosome separation according to claim 1, characterized in that: Determining the target state of the target exosome according to the dissimilation value includes: The state value of the target exosome is calculated according to the dissimilation value, wherein the state value calculation formula is: in, is the state value, is the alienation value, is the feedback error value; When the state value is a preset first target state threshold, the target state of the target exosome is a normal state; When the state value is a preset second target state threshold, the target state of the target exosome is an abnormal state.

8. The method for intelligently identifying non-exosome particles in exosome separation according to claim 1, characterized in that: The generating of an abnormal feedback matrix according to the target state and preset exosome category data includes: Extracting exosome category features from preset exosome category data; Comparing the multidimensional features of the target exosomes with the class features of the exosomes to obtain a feature feedback value; An abnormal feedback matrix is ​​generated according to the target state and the characteristic feedback value.

9. An intelligent identification system for non-exosome particles used in exosome separation, characterized in that: The system comprises: An exosome separation module, used to obtain an exosome sample, and generate a multidimensional parameter gradient through a preset multidimensional separation parameter, including: extracting a physical separation parameter and a chemical separation parameter from the preset multidimensional separation parameter; using the physical separation parameter and the chemical separation parameter as a first-level parameter gradient; extracting a second-level parameter gradient from the chemical separation parameter; generating a multidimensional parameter gradient according to the first-level parameter gradient and the second-level parameter gradient; and performing exosome separation on the exosome sample using the multidimensional parameter gradient to obtain target exosomes; A feature fusion module, used to extract the multidimensional features of the target exosomes, and perform feature fusion on the multidimensional features to obtain the multidimensional fusion features of the target exosomes; A multidimensional fusion feature classification module, used to perform feature classification on the multidimensional fusion feature using a pre-built multidimensional decision tree recognition model to obtain a feature classification set of the target exosomes, wherein the multidimensional decision tree recognition model is determined according to the number of features in the multidimensional fusion feature, each decision tree branch corresponds to a feature, and the feature category corresponding to each feature is detected, and the preset decision tree model is trained through historical feature data, and continuously optimized until the optimization conditions are met, and the output decision tree model is a multidimensional decision tree recognition model; a target state determination module, configured to calculate the alienation value of the feature classification set by using a preset alienation weight algorithm, and determine the target state of the target exosome according to the alienation value; A non-exosome particle identification module is used to generate an abnormal feedback matrix according to the target state and preset exosome category data, and identify the non-exosome particles in the target exosomes according to the abnormal feedback matrix, wherein the identifying the non-exosome particles in the target exosomes according to the abnormal feedback matrix includes: calculating the target attribute value of the target exosome according to the feedback value and the state value in the abnormal feedback matrix: in, is the target attribute value, is the feedback value, is the state value, is the feedback threshold, is the state threshold; When the target attribute value is a preset target attribute threshold, the target exosome corresponding to the target attribute value is determined as a non-exosome particle.

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