63 results about "Differential expression analysis" patented technology

The invention relates to the technical field of bioinformatics, and provides a crop whole genome phenotype prediction method and system fused with an environmental indicator gene, and the method comprises the following steps: collecting re-sequencing data, and carrying out genome comparison to obtain variation site data; performing whole genome association analysis by using the variation site data to obtain phenotype association site information; carrying out gene expression quantity measurement on samples of the crop population material in different environments to obtain gene expression quantity data; performing differential expression analysis on the gene expression quantity data to screen environmental indicator genes to obtain an environmental indicator gene set; constructing a phenotype prediction model of double-branch fusion; and predicting a to-be-predicted material through the phenotype prediction model to obtain phenotype prediction results for different environments. According to the method, environmental factors are incorporated into the whole genome selection model, so that the phenotype prediction precision in different environments is improved.

The invention provides an immunotherapy reaction marker identification method and system based on a cooperative game theory, and relates to the technical field of intelligent medical treatment, and the method comprises the following steps: obtaining immunotherapy single cell sequencing data, and determining a candidate gene set through differential expression analysis; extracting regulation and control relation pairs of the candidate gene set to obtain a multi-level information gene regulation and control network; embedding the multilevel information gene regulation and control network by adopting a Node2vec algorithm to obtain a network module containing potential biological functions, calculating the contribution degree of each gene feature to model prediction based on a Myerson value, and obtaining a feature importance sequence; combining the optimal features of the modules into a global candidate set; and iteratively optimizing the global candidate set, and outputting a target immunotherapy reaction marker set. Through a Myerson value principle in a cooperative game, a biomarker set related to immunotherapy response is efficiently searched, deep analysis of a drug resistance mechanism is realized, the accuracy and robustness of marker screening are improved, and the method serves for cancer clinical scheme customization.

The invention belongs to the technical field of biological detection, and discloses a screening method of respiratory tract infection related biomarkers based on metatranscriptomics. Respiratory tract infection samples with different clinical phenotypes are subjected to metatranscriptome sequencing, data quality control, comparison and transcript quantification, pathogen and host information is reserved, then genes which are stably expressed or remarkably changed in different groups are identified by combining differential expression analysis and co-expression analysis, and potential biomarkers are obtained. And taking an intersection gene of three machine learning algorithms including an LASSO algorithm, a random forest model and an SVM model to obtain the biomarker. The invention provides a biomarker screening method for rapid and accurate identification of respiratory tract infection.

The application belongs to the technical field of biological detection, and discloses a screening method of respiratory tract infection related biomarkers based on macro-transcriptomics. The application performs macro-transcriptome sequencing on respiratory tract infection samples with different clinical phenotypes, performs data quality control, alignment, transcript quantification, retains pathogen and host information, and then identifies genes stably expressed or significantly changed in different groups by combining differential expression analysis and co-expression analysis, obtains potential biomarkers, and obtains the biomarkers by taking the intersection genes of three machine learning algorithms of LASSO algorithm, random forest model and SVM model. The application provides a screening method of biomarkers for rapid and accurate identification of respiratory tract infection.

The invention discloses a screening method of a biomarker for auxiliary diagnosis of small cell lung cancer, and relates to the technical field of biomedicine. The method comprises: acquiring multiple groups of samples; the plurality of groups of samples comprise exosome RNA transcriptome sequencing data of a plurality of SCLC patients and a plurality of healthy controls; each piece of exosome RNA transcriptome sequencing data comprises a plurality of RNA characteristics; carrying out technical quality filtering on RNA characteristics in all exosome RNA transcriptome sequencing data, carrying out differential expression analysis on the filtered characteristics, and determining candidate RNA sets of the SCLC patient and the healthy contrast; performing feature selection on the candidate RNA set through three complementary feature selection modes, and screening an optimal exosome RNA marker combination from different quantities of RNA feature combinations through 20 times of iteration and 10-fold nested cross validation; and the optimal exosome RNA marker combination comprises LINC00989, CXCL5, MAP3K7CL and TUBB1 (Tumor Umbrella Blanket B1). The optimal exosome RNA marker combination screened by the method is beneficial to diagnosis of small cell lung cancer.

The invention provides a cancer diagnosis model development method. The cancer diagnosis model development method comprises the steps of constructing a training set and establishing a diagnosis model on the training set. The training set includes miRNA expression profiles from non-cancer subjects and cancer patients having two or more cancer types, and establishing the diagnostic model includes calculating a diagnostic index based on a selected miRNA biomarker set, the selected miRNA biomarker set being obtained from a miRNA ranking in a differential expression analysis of the miRNA expression profiles in the training set. Methods of detecting a target cancer by means of the diagnostic model so developed are also provided. A 4-miRNA-based diagnostic model shows high performance in a verification set, and can realize the sensitivity greater than or equal to 0.98 while maintaining the specificity of 0.99 when detecting various cancers including lung cancer, gastric cancer, biliary tract cancer, bladder cancer, prostate cancer and glioma.

The invention discloses a heart failure key gene identification method and system based on a spline regression model and overall entropy change. The method comprises the following steps of: 1, preprocessing data; 2, analyzing time sequence differential expression; 3, constructing a dynamic co-expression network; 4, network overall entropy change analysis; and 5, screening the heart failure key gene. Compared with an existing analysis scheme in which time sequence differential expression analysis and network topology analysis are mutually independent, a coherent technical system from'capturing gene expression time sequence trend 'to'quantifying gene network overall structure evolution' and then'screening key genes' is constructed. The thought jumps out of the limitation of a single method dimension, and more fitting and more systematic analysis of the continuous nonlinear evolution process of the heart failure are realized. Meanwhile, the spline regression model is matched with dynamic network overall entropy change analysis, so that the synergistic effect of effectively screening and comprehensively analyzing differential genes is achieved.

This invention discloses a wheat blue-grain gene and its applications, belonging to the fields of plant molecular biology, biochemistry, genetics, and plant breeding. Using differential expression analysis between blue-grain and white-grain wheat, four genes controlling the blue-grain trait were identified: two MYB family transcription factors and two bHLH family transcription factors. Furthermore, plant recombinant expression vectors for these genes and a method for regulating anthocyanin synthesis in plants are provided. This has significant theoretical and practical implications for studying the aleurone layer pigment synthesis pathway in blue-grain wheat, its use as a screening marker in plant transformation processes, and improving the nutritional value of plants.

The invention relates to application of HLA-DR < + > tumor cells in evaluation of liver cancer prognosis or immunotherapy response, and belongs to the technical field of tumor biomarkers and prognosis evaluation. The technical problem to be solved by the invention is to provide a biomarker for evaluating liver cancer prognosis or immunotherapy response, namely HLA-DR < + > tumor cells. Through scRNA-seq and accurate differential expression analysis, gene expression characteristics of HLA-DR < + > tumor cells in HBV-HCC are disclosed for the first time, and a high-specificity molecular marker is provided for prognosis evaluation or immunotherapy response prediction. A prognosis scoring model is constructed by using the molecular marker, so that the prognosis prediction accuracy of HBV-HCC patients can be improved, and a scientific basis is provided for clinical precision medical treatment. An immune response scoring model is constructed by using the molecular marker, and is used for predicting the treatment response of a patient to a PD-1 / PD-L1 inhibitor and guiding clinical formulation of an individualized treatment scheme. In addition, the invention also helps to deeply reveal an immune escape mechanism of HBV liver cancer, and lays a foundation for subsequent immunotherapy strategy development of targeted HLA-DR + tumor cells.

The invention discloses a one-stop tumor RNA-seq analysis system which comprises an original data quality control module, a sequence comparison and quantification module, a fusion gene analysis module, a variable shear analysis module, a differential expression analysis module, a function enrichment analysis module, an immune pathway module, a data management and visualization module and a containerization safety deployment module. The system receives an FASTQ format of original RNA-seq data, carries out quality evaluation and filtering, generates high-quality clean reads, generates gene expression quantitative data through sequence alignment, carries out differential expression, function enrichment and other analysis on the gene expression quantitative data, and carries out data management, visualization and safe deployment on an analysis result. The analysis system is high in data analysis efficiency and credible in analysis result, so that scientific researchers and biomedical workers can perform tumor RNA-seq data analysis more conveniently.

The present invention relates to a prognostic risk prediction model for colorectal cancer. The model is constructed through the following steps: obtaining the gene expression profiles and clinical information data of colorectal cancer patients, and the data for model construction and validation includes a training set and a validation set; performing differential expression analysis on the gene expression matrix to obtain differentially expressed genes; performing univariate Cox regression analysis on the differentially expressed genes in the training set to screen out prognosis-related genes; performing LASSO Cox regression analysis on the screened genes, determining the optimal λ value through cross-validation, screening out the genes that make up the model, and calculating the risk scores of each patient according to the regression coefficients of the genes and their expression levels in the training set and the validation set; dividing the patients into high- and low-risk groups according to the risk scores, and evaluating the prediction performance of the prediction model through Kaplan-Meier survival curves, time-dependent ROC curves, etc. The present invention can bring new strategies for predicting the survival time of colorectal cancer patients and the sensitivity to chemotherapeutic drugs.

The invention discloses a breast cancer heterogeneity analysis system based on unicellular omics and Mendel randomization, and relates to the technical field of biological information. Comprising a data acquisition module, a single cell data processing module, a differential expression analysis module, a Mendel randomization analysis module, a survival analysis module, a function enrichment module, a drug prediction module and an output module. The system is used for integrating multiple omics data and identifying causal driving genes in breast cancer malignant epithelial cell subgroups. According to the system, unicellular omics and Mendel randomization are combined, specific causal inference of breast cancer heterogeneity cell types is achieved, key genes are identified, part of the genes are positively or negatively correlated with risks, and clinical significance is verified through survival analysis; the system provides new biomarkers and targets for accurate treatment of breast cancer, and has the advantages of high precision, strong repeatability and large clinical transformation potential.

The invention provides application of a biomarker in preparation of a detection product for diabetic sarcopenia, and relates to the technical field of diagnostic products for diabetic sarcopenia. The biomarker is BOLA3, skeletal muscle transcriptome data of a normal individual and a type 2 diabetes patient (T2D) are integrated, after strict quality control and homogenization treatment, differential expression analysis is performed by using a lima packet, and a T2D skeletal muscle differential expression gene set is obtained. Core module genes obviously related to the phenotype of the diabetic sarcopenia are screened out through weighted gene co-expression network analysis. Combining an iron metabolism and ferroptosis related gene set in a database, taking an intersection of the differential gene, the WGCNA module gene and the iron metabolism gene set, and finally positioning 45 hub genes. A random forest algorithm and a neural network algorithm are adopted to finally determine that BOLA3 is a key biomarker of the diabetic sarcopenia, and a new direction is provided for diagnosis and treatment of the diabetic sarcopenia.

The invention provides a rice heat-resistant candidate gene screening method based on population inheritance and transcriptome integration, which comprises the following steps: acquiring a rice population, performing population differentiation analysis on the rice population, and screening out highly differentiated sites. And carrying out heterozygosity screening on the rice population to obtain a target SNP site. And taking a target SNP site which is obviously differentiated among the subgroups and is homozygous in the subgroups as a candidate SNP site. False positive signals caused by genetic background differences can be effectively eliminated through combined screening of genetic differentiation between subgroups and homozygosity in groups. And performing function annotation association on the candidate SNP sites, and performing transcriptome differential expression analysis on the rice population. And determining a final candidate gene according to the potential regulatory gene and the differential expression gene. Through collaborative analysis of population genetics and transcriptomics, genetic differentiation and functional expression are verified at the same time, the accuracy and reliability of candidate genes can be remarkably improved, and limitation of single-dimension screening is avoided.

The application discloses tomatoes lncrna-pel8 In the application, GWAS correlation analysis and lncRNA differential expression analysis are used to quickly and effectively identify lncRNAs related to metabolites of tomato fruits, including lncRNAs related to the content of 2-phenylethanol lncrna-pel8 , lncrna-pel8 The expression of the lncRNAs can be used for efficiently screening germplasm resources with different 2-phenylethanol contents, and assisting in tomato quality breeding. lncrna-pel8 The identification of the lncRNAs enriches and perfects an existing 2-phenylethanol synthesis and regulation network, and reveals a molecular mechanism of changes in the content of 2-phenylethanol in the evolution process of tomatoes.

The application discloses a paclitaxel treatment response prediction method and system for triple-negative breast cancer patients, and the method comprises the following steps: obtaining single-cell transcriptome data, bulk transcriptome data and paclitaxel sensitivity data of triple-negative breast cancer patients before and after treatment and preprocessing; constructing a regression model of immune microenvironment and tumor response, calculating the response score of immune cell subgroups, screening key immune cell subgroups related to paclitaxel treatment response, performing differential expression analysis on the pre-treatment sample, constructing a paclitaxel IC50 prediction model, and dividing the sample into a high-sensitivity group and a low-sensitivity group; constructing a treatment response prediction model and outputting a prediction result. The technical scheme integrates the dynamic changes of the immune microenvironment, the transcriptome characteristics and the drug sensitivity data, realizes accurate prediction of the paclitaxel treatment response of the triple-negative breast cancer patients before treatment, and provides a scientific basis for formulating an individualized treatment plan.

The invention relates to the technical field of bioinformatics and medical diagnosis, in particular to a method for screening bipolar affective disorder and epilepsy co-pathogenic biomarkers, which comprises the following steps of: performing differential expression analysis on known gene chip expression profile data and finding out a significant correlation module by using a WGCNA algorithm so as to determine key differential genes; finding out hub genes in the core differential genes by using a PPI network, and screening out candidate core pathogenic genes of the bipolar affective disorder and the epilepsy by taking the result intersection of three machine learning methods of LASSO regression, support vector machine recursive feature elimination and random forest; the screened marker can be used for prediction and early prediction of bipolar affective disorder and epilepsy, early screening can be better completed, and a new perspective is provided for molecular mechanism research and early diagnosis of bipolar affective disorder and epilepsy co-pathopoiesis.

The embodiment of the invention discloses a gene expression analysis method and device, electronic equipment and a readable storage medium, and belongs to the technical field of bioinformatics and the field of data processing. The method comprises the following steps: based on a polymerization expression value of a gene in a target cell type of a to-be-detected sample and a benchmark expression value of a gene in a target cell type of a reference sample, determining a first difference of the target gene in the target cell type in the to-be-detected sample relative to the target gene in the reference sample; determining a second difference of the target genes in the target cell type according to the first difference of the target genes in the target cell type and the first difference of the target genes in different cell types of the to-be-detected sample; and performing gene differential expression analysis based on the second difference of the target gene. Based on the embodiment of the invention, the influence of a batch effect on gene expression analysis can be effectively reduced.

The invention discloses a proteome marker identification method and system, electronic equipment and a storage medium, and the method comprises the steps: adding a one-hot coded grouping tag to protein abundance data in mass spectrum quantitative data after data preprocessing, and coding group information corresponding to the protein abundance data into a binary vector; performing supervised training on the data enhancement model, so that a logarithmic variance output by an encoder of the data enhancement model is cut to be within a range of (-10, 10), and generating new sample data by using a decoder of the trained data enhancement model; according to the result of principal component analysis, dividing the new sample data with the similarity reaching a set threshold value into a group set, identifying potential markers through the group set by utilizing a differential expression analysis or random forest method, sorting the potential markers according to the difference multiple or importance parameters output by a specified data filling model, and obtaining the potential markers. And multi-group exosome proteome marker identification results are obtained. According to the invention, the accuracy and reliability of marker identification are improved.

The invention discloses application of porcine membrane protein EPG5 in identifying and distinguishing porcine Y sperms. According to the new application of the porcine membrane protein EPG5 provided by the invention, the porcine X and Y sperm membrane proteins are subjected to proteomics analysis through a DIA proteomics technology, the porcine membrane protein EPG5 differentially expressed in the Y sperm membrane protein is screened out, the expression of the protein on the Y sperm is obviously higher than that of the X sperm, and the protein can be used as a molecular marker to identify and distinguish the porcine X and Y sperms. Research shows that the X and Y sperms of the pig can be accurately identified and distinguished through differential expression analysis of the porcine membrane protein EPG5, and the result is consistent with the sorting result of a flow cytometry. The invention provides new application of the porcine membrane protein EPG5 in identifying and distinguishing the porcine Y sperms, protein resources are expanded, more and better methods and selections are provided for identifying and distinguishing the porcine Y sperms, sperm sex sorting is performed through differential expression of the porcine Y sperm membrane protein EPG5, and the method has the advantages of low cost, small damage, high efficiency, simplicity and convenience and good application prospect.

This invention provides a method and system for processing hepatocellular carcinoma data. The method utilizes the CIBERSORT tool combined with weighted gene co-expression network analysis to screen a gene set positively correlated with M2 macrophage infiltration. Through differential expression analysis and prognostic correlation analysis, target genes with prognostic value are identified from the gene set. A dataset containing the target genes is acquired, and a comprehensive machine learning algorithm is used to generate several corresponding prognostic prediction models for each dataset. The C-index of the dataset in the corresponding prognostic prediction model is calculated, and target prognostic prediction models are selected based on the C-index and the complexity of the prognostic prediction model. Compared to the traditional method of selecting an algorithm for modeling at the beginning of the study, the final selected target prognostic prediction model reflects more objective and realistic prediction results. Furthermore, the prediction accuracy of this target prognostic prediction model is improved.

This invention discloses a method for verifying TBCB glioblastoma and cell proliferation, belonging to the fields of bioinformatics and tumor molecular biology. S1: Constructing a TBCB knockdown model and verifying its effectiveness; S2: Obtaining transcriptome data from the knockdown group and control group using RNA-seq; S3: Obtaining a set of DEGs through differential expression analysis; S4: Performing disease enrichment analysis based on DisGeNET to obtain significantly enriched disease entries and screening for GBM / glioma-related entries; S5: Verifying the expression differences and clinical relevance of TBCB and GBM based on public databases (one or more of GEO / TCGA / CGGA); S6: Conducting an EdU incorporation experiment in U87 cells to quantitatively verify the effect of TBCB knockdown on cell proliferation. The verification method of this invention is more directly disease-targeting: by using DisGeNET disease enrichment, the DEGs set is directly mapped to specific disease entries such as "glioma / glioma," avoiding a focus solely on pathway levels.

The invention relates to a judgment method using a BMPR2 gene as a marker of lupus nephritis, which comprises the following steps: collecting a clinical kidney sample of lupus nephritis, preparing a nuclear suspension, putting the nuclear suspension, bar code gel beads and oil into a system for treatment, generating a gel bead emulsion, carrying out reverse transcription on the gel bead emulsion, and detecting the BMPR2 gene. The method comprises the following steps of: evaluating cDNA concentration and fragment size, performing cDNA amplification, performing enzyme digestion fragmentation, selecting an optimal fragment according to the size of a magnetic bead, preparing a library, purifying by using the magnetic bead, quantifying and evaluating the fragment size by using an instrument, generating a cluster and hybridizing an initial primer, loading flowing cells bearing the cluster onto a sequencer, and performing bidirectional sequencing. In conclusion, the BMPR2 gene expression quantity is used as a marker for suffering from the lupus nephritis, the condition of the lupus nephritis is judged by detecting the BMPR2 gene expression quantity in kidney tissue, and the kit has the advantages of being convenient to use and good in detection effect.

The application discloses a screening method of biomarkers for assisting in diagnosing small cell lung cancer, and relates to the technical field of biomedicine. The method acquires multiple groups of samples; the multiple groups of samples include exosome RNA transcriptome sequencing data of multiple SCLC patients and multiple healthy controls; each exosome RNA transcriptome sequencing data includes multiple RNA features; technical quality filtering is performed on the RNA features in all exosome RNA transcriptome sequencing data, and differential expression analysis is performed on the filtered features to determine a candidate RNA set of the SCLC patients and the healthy controls; feature selection is performed on the candidate RNA set through three complementary feature selection methods, and through 20 iterations and 10-fold nested cross-validation, optimal exosome RNA marker combinations are screened from different combinations of RNA features; the optimal exosome RNA marker combinations include LINC00989, CXCL5, MAP3K7CL and TUBB1. The optimal exosome RNA marker combinations screened by the method are beneficial to the diagnosis of small cell lung cancer.

This invention discloses a prognostic model and construction method for cervical squamous cell carcinoma, belonging to the fields of bioinformatics and oncology. The construction method is based on a set of genes related to programmed cell death, integrating multi-omics data of cervical squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the Cancer Genome Characterization Project (CGCI). After standardized preprocessing, differential expression analysis, bootstrap resampling combined with univariate Cox regression for initial screening, consensus screening using multiple machine learning algorithms, and finally, multivariate Cox proportional hazards regression analysis to identify three core independent prognostic genes and construct a risk scoring model. This model has undergone multi-dimensional validation and optimization, demonstrating robust predictive performance. The method of this invention is standardized and highly reproducible, and the constructed model has high accuracy and strong generalization ability, providing a reliable tool for individualized prognostic assessment and clinical decision-making for cervical squamous cell carcinoma patients.

The embodiment of the invention provides a cell subset division optimization method and device based on a single cell clustering result. The method is applied to the technical field of medical data analysis, and comprises the following steps: performing clustering analysis on single-cell RNA sequencing data according to a preset initial clustering resolution to obtain a plurality of cell subgroups after preliminary clustering; performing differential expression analysis on each cell subset obtained by preliminary clustering to obtain a differential expression gene quantity corresponding to each cell subset; and under the condition that the cell subgroups with the differential expression gene quantity lower than a preset threshold value exist in all the cell subgroups obtained by the preliminary clustering, performing automatic iteration merging on the cell subgroups obtained by the preliminary clustering until the differential expression gene quantity of all the merged cell subgroups is not lower than the preset threshold value. And outputting an optimized cell subset division result. According to the method, the accuracy of cell subset division and the efficiency of single cell data analysis are improved, and the biological rationality and interpretability of an analysis result are improved.

The application belongs to the technical field of computers and provides a tumor differential gene expression profile prediction method and system combined with pathological omics features. The model training method comprises the following steps: obtaining the nucleus feature spectrum of the specific tumor area of the digital pathology section according to the data pathology section data; obtaining the cohort gene expression matrix according to the transcriptomics data; determining the clinical diagnosis stage of the cancer tissue and normal tissue sample and the cancer sample according to the cohort gene expression matrix and the patient clinical information, and performing gene differential expression analysis to obtain the differential gene expression profile of the specific tumor and the differential gene expression profile of the specific tumor stage; and taking the nucleus feature spectrum of the specific tumor area of the digital pathology section as the input and taking the differential gene expression profile of the specific tumor and the differential gene expression profile of the specific tumor stage as the output to train a generative model. The application greatly reduces the sample sequencing cost and improves the subsequent diagnosis and prognosis efficiency.

The invention provides a semi-supervision-based omics data batch effect correction method. A batch effect is removed in two stages by adopting an auto-encoder architecture. In the first stage, an encoder is optimized through a semi-supervised strategy, and low-dimensional embedding representation after batch correction is generated; in the second stage, encoder parameters are fixed, decoder optimization is further guided by means of correlation information represented in an embedded mode, and an expression matrix after batch correction is obtained. According to the method, the low-dimensional representation after batch correction can be generated, the corrected expression matrix can be reconstructed, and the requirements of various downstream tasks such as subsequent clustering and differential expression analysis are met.

The application discloses a bioinformatics analysis method for identifying a brain hemorrhage prognosis-related gene target point, and the method comprises the following steps: obtaining brain hemorrhage transcriptome expression data, performing quality filtering and batch correction to generate a standardized expression matrix, performing differential expression analysis on the basis of the standardized expression matrix, screening prognosis-associated differential genes in combination with survival regression, constructing a weighted co-expression network to identify a prognosis key gene cluster, screening and verifying a core hub gene through hub degree, taking the intersection of the prognosis-associated differential genes and the core hub gene, filtering and interaction network clustering a core target gene through a pathological pathway, performing immune infiltration correlation analysis and survival analysis on the basis of the core target gene, and outputting the brain hemorrhage prognosis-related gene target point according to an immune correlation marker and a survival correlation index. The application integrates a differential analysis and a network analysis double strategy, confirms mechanism correlation through pathway verification and immune correlation analysis, and the screening result has statistical reliability and biological interpretability.

The embodiment of the invention discloses an artificial intelligence and biosensing key quality attribute identification method of a blood activating and heterosis eliminating prescription. The method comprises the following steps: collecting a plurality of groups of clinical serum samples and sequencing; carrying out data processing and differential expression analysis on the multiple groups of sequencing results by adopting a machine learning algorithm, and screening out key RNA; acquiring a multi-component chemical component data set of the blood-activating and heterosis-eliminating prescription, and acquiring an action target of a targeted disease of the blood-activating and heterosis-eliminating prescription; carrying out enrichment analysis on the key RNA and the action target spot, screening out a plurality of potential target spots, and carrying out verification analysis; carrying out molecular docking on the protein structure files of the plurality of potential target spots and the structure file of the blood-activating and anisotropic-eliminating prescription; constructing a target protein biosensor; acquiring a sample solution of the blood-activating and heterosis-eliminating prescription, and detecting the interaction strength of the sample solution and the target protein biosensor; eluting the target protein biosensor to obtain a protein eluent for promoting blood circulation and eliminating heterosis; and identifying the key quality attributes of the protein eluent of the blood-activating and heterosis-eliminating formula by using a UPLC-MS (ultra performance liquid chromatography-mass spectrometry) technology.