114 results about "Differentially expressed genes" patented technology

A spatial omics multi-modal fusion method under a single cell level comprises the following steps: extracting spatial morphological characteristics of differential expression genes and cell nucleuses from spatial transcriptome data, single cell sequencing data and histological images, and realizing field adaptation among different platforms by using a conditional variation auto-encoder. And based on a probability inference model, fusing spatial transcriptome expression, unicellular omics and morphological characteristics, and jointly inferring the type and gene expression level of each cell. A spatial cell network is constructed through a graph attention mechanism, and spatial diffusion and recognition of cell types in a full slice range are realized. In combination with a multi-omics enhancement module, undetected gene and protein expression is completed based on expression similarity, and prediction consistency is improved through spatial correction. According to the method, high-resolution reconstruction of single-cell multi-omics information in a three-dimensional space is realized, the information coverage and spatial resolution of spatial omics data are improved, and an efficient and low-cost solution is provided for spatial biology and precise medical research.

The invention provides a gene module analysis method and device, equipment and a storage medium, and relates to the technical field of space transcriptomics. The method comprises the steps of obtaining space-time group chip expression data, performing linearization processing, and performing sliding window cutting to obtain a plurality of window units arranged according to a space sequence; calculating gene function activeness and constructing a matrix; clustering and grouping are carried out to obtain window groups; performing difference analysis screening on the window groups to obtain a difference expression gene set; and performing gene module analysis to obtain a functional gene module. According to the method, through an integrated processing flow of structure linearization, sliding window cutting, activeness quantification, clustering analysis and module identification, fine extraction and function reconstruction of spatial expression information in the curled tissue are realized under the condition that an additional algorithm or external data is not introduced; and the practicability and research value of the spatial omics data in a complex organization structure are improved.

A disease marker expression regulatory mechanism prediction method comprises the following steps: acquiring gene expression data, performing quality control and standardization processing on the gene expression data to obtain standardized gene expression data, identifying differentially expressed genes as candidate markers, and predicting a disease marker expression regulatory mechanism. A transcriptional regulation and control network of the candidate markers is constructed, transcription factor data are generated, a multi-layer neural network model is established through a four-quadrant neural network model, and the four-quadrant neural network model comprises a contribution value calculation layer, a statistical test layer, a quadrant division layer, a feature integration layer and a comprehensive output module. The four-quadrant neural network model adopts an adaptive quadrant division optimization equation set to carry out feature analysis and prediction on the candidate markers; compared with the prior art, the method is remarkably improved in the aspects of feature integration, dynamic modeling, self-adaptive optimization and the like, and important theoretical basis and technical support are provided for disease diagnosis, prognosis evaluation and treatment strategy formulation.

The invention discloses a rape grain weight regulation gene NGAL3 based on whole genome screening and a breeding application thereof, based on the seed size and grain weight phenotypic value of a cabbage type rape germplasm resource, a candidate gene interval is positioned by using GWAS; the method comprises the following steps: selecting large-grain-weight and small-grain-weight extreme phenotypic materials, performing transcriptome sequencing in a critical period of seed development, and screening differential expression genes; and performing cross comparison on the GWAS candidate gene and the differential expression gene to obtain a key gene for jointly regulating and controlling the size and the grain weight of the seed, and performing functional verification on the key gene. The thousand seed weights of the created homozygous three mutant strains L1-sg1-1-4-5 and L2-sg1-4-8-16 are obviously increased by 46% and 29% compared with those of the wild type strains. The invention provides a new target for high-yield rape breeding, and non-transgenic high-grain-weight germplasm can be created through gene editing or beneficial allelic variation of NGAL3 is selected and enriched under the assistance of molecular markers.

The invention belongs to the technical field of medicines, and particularly relates to application of BMP9 as an intervention target in preparation of a medicine for treating diabetic nephropathy. More specifically, the invention provides application of a BMP9 accelerant in preparation of a medicine for preventing and / or treating diabetic nephropathy. The BMP9 accelerant is a substance shown in the following (i) or (ii): (i) a substance for increasing the content of BMP9 in a subject; (ii) a substance that increases BMP9 activity in a subject. According to the invention, a DKD mouse model is constructed, and liver RNA-seq sequencing is carried out, such that a differential expression gene BMP9 exists between a normal mouse and a DKD mouse. It is found that BMP9 can be used as a new target for prevention and treatment of DKD and has important theoretical significance and potential clinical application value.

The invention discloses an adversity high-photosynthetic-efficiency transcription factor screening method based on deep learning, and relates to the technical field of biological information analys.The method comprises the steps that rice multi-modal stress response data is obtained and preprocessed, and preprocessed gene expression data is obtained; carrying out differential expression gene screening and co-expression network analysis on the preprocessed gene expression data, extracting multi-modal features, and fusing the multi-modal features to generate a multi-modal input feature matrix; constructing a double-layer deep learning model, training the double-layer deep learning model by using the multi-modal input feature matrix, and respectively outputting a regulation and control relationship matrix of transcription factors and target genes and a regulation and control relationship matrix of transcription factors and target pathways; and according to an output result, calculating a comprehensive score of each transcription factor through a multi-dimensional scoring system, and screening out the stress high-photosynthetic-efficiency transcription factor according to a predetermined screening standard.

This application relates to the field of machine learning technology, and discloses a method, apparatus, device, and storage medium for predicting the efficacy of cancer treatment. The method includes: extracting radiomics features from CT scan images to obtain radiomics features; extracting pathological features of interest from pathological images based on an attention mechanism and extracting local pathological features based on the results of the extracted pathological features of interest to obtain pathomics features; performing differentially expressed gene analysis on transcriptomics data and screening for differentially expressed genes based on the results of the differentially expressed gene analysis to obtain transcriptomics features; and inputting the fused features obtained by fusing radiomics features, pathomics features, transcriptomics features, and clinical data into a cancer treatment efficacy prediction model to obtain a cancer treatment efficacy prediction result. The embodiments of this application combine radiomics features, pathomics features, transcriptomics features, and clinical features to predict cancer treatment efficacy, which can improve prediction accuracy.

This invention discloses a method for constructing a prognostic model for acute myeloid leukemia (AML) based on ferroptosis-related genes. The method involves acquiring gene expression data from AML patients and healthy samples to screen for differentially expressed ferroptosis-related genes (DEGs) associated with AML. Using univariate Cox proportional hazards regression analysis, LASSO regression analysis, and multivariate Cox proportional hazards regression analysis, key genes ACSF2, SLC7A11, DNAJB6, and SOCS1 are selected from these DEGs. A prognostic risk model is constructed based on the expression levels of these key genes and their corresponding multivariate Cox regression coefficients. The risk score formula is: Risk Score = 0.534 × ACSF2 expression value - 0.453 × DNAJB6 expression value + 0.194 × SLC7A11 expression value + 0.308 × SOCS1 expression value. The prognostic model constructed in this invention has high predictive accuracy and reliability, effectively stratifying the risk and assessing the prognosis of AML patients, providing an important reference for the clinical treatment of AML.

The invention relates to the technical field of biology, in particular to a gene co-expression network-based sepsis marker screening method and application. The screening method comprises the following steps: screening differential expression genes; performing hierarchical clustering analysis on each differential expression gene to combine each differential expression gene into a plurality of target gene modules; and screening out hub genes from each target gene module. According to the method, gene expression data and clinical feature data are combined, the accuracy of screening sepsis marker genes is improved, possible abnormal genes irrelevant to the pathophysiological process of sepsis are avoided, gene modules are divided more finely through primary clustering analysis and secondary clustering analysis, GO function analysis is combined, and the accuracy of screening the sepsis marker genes is improved. According to an analysis result, selection of a GO term subset is optimized, and a step length set by primary clustering is corrected, so that the fine granularity and accuracy of analysis are improved, division of gene modules is optimized, and more refined treatment and screening of markers of sepsis are realized.

The invention relates to application of an ILF3 gene in diagnosis or treatment of sepsis, and belongs to the technical field of biological medicines. It is found for the first time that the ILF3 gene is a macrophage-related differential expression gene of sepsis, the expression level of the ILF3 gene in plasma and alveolar lavage fluid of sepsis patients is obviously higher than that of healthy people, a primer pair capable of specifically recognizing and detecting the ILF3 gene is designed, a mouse with the ILF3 gene in macrophage being specifically knocked out is constructed, and the ILF3 gene in the macrophage is specifically knocked out. The invention also designs si-ILF3 capable of specifically interfering with the expression level of the ILF3 gene. The ILF3 gene is used as a sepsis diagnosis marker and a sepsis treatment target, the application of the ILF3 gene detection reagent in preparation of sepsis diagnosis products and the application of si-ILF3 in preparation of sepsis treatment drugs are provided, and the si-ILF3 gene detection reagent is used for further diagnosis and treatment of sepsis patients.

The invention discloses a method for researching influence on hemolytic activity of marine microalgae based on a transcriptome technology, and relates to the field of hemolytic activity analys.The method comprises the steps that pretreatment is conducted on the marine microalgae based on culture requirements, and cystic morphological cell density and hemolytic activity of the marine microalgae are measured according to experimental design rules after pretreatment is completed; carrying out transcriptome sample collection operation by utilizing the marine microalgae subjected to pretreatment, constructing a library according to a transcriptome sample collection result, and obtaining a gene function annotation and a differential expression gene result; the growth conditions of the marine microalgae under different temperature conditions are analyzed according to cystic morphological cell density and hemolytic activity, and the regulatory gene influencing the hemolytic activity of the marine microalgae is obtained by combining gene function annotation and differential expression gene results. According to the method, the hemolytic activity of the marine microalgae cultured under different temperature conditions is extracted, so that the aim of researching related metabolic pathways possibly participating in toxin synthesis and hemolytic activity regulation is fulfilled.

The application discloses a method for screening garlic fructan adversity response key enzyme genes and metabolites based on transcriptome and metabolome, and the method can better analyze the relationship between genes and metabolites by jointly analyzing the fructan-related differentially expressed genes measured by the transcriptome and the fructan-related metabolism substances with obvious metabolism changes measured by the metabolome, and constructing a gene-metabolite network interaction diagram.

The present invention is applicable to the field of genetic engineering technology and provides a cloning and application of the Sophora alopecuroides (Solanum sophora) SaPEI7 gene. By screening differentially expressed genes from transcriptome sequencing of Sophora alopecuroides under simulated stress conditions, a Sophora alopecuroides gene related to salt tolerance was identified through bioinformatics analysis as the Sophora alopecuroides (Solanum sophora) pectin methylesterase inhibitor protein gene SaPEI7. Quantitative detection using RT-PCR technology revealed a significant increase in gene expression under salt stress conditions. The gene was functionally verified by constructing a plant expression vector and successfully transformed into Arabidopsis thaliana. The results showed that overexpression of the gene in Arabidopsis thaliana improved its salt tolerance, providing a new resource for improving crop stress resistance through genetic engineering technology.

The invention provides a feature gene selection method and system based on deep learning attribution analysis and beam combination optimization, and relates to the technical field of signal analysis, and the method comprises the following steps: obtaining single cell RNA sequencing data and batch RNA sequencing data; the method comprises the following steps: based on single-cell RNA sequencing data, obtaining an initial gene pool aiming at a plurality of disease subtypes through a consensus screening strategy fusing XGBoost multi-dimensional importance measurement and deep learning SHAP attribution analysis; performing first-stage optimization on the gene list of each subtype by taking the initial gene pool as a starting point and adopting beam combination search and based on a global discrimination objective function to obtain a first-stage optimized gene set; integrating the first-stage optimized gene set with the differential expression gene set of the batch RNA sequencing data, and carrying out second-stage optimization by adopting bundle combination search again to obtain a second-stage optimized gene set; and performing cross validation integration on the second-stage optimized gene set, and outputting a final feature gene set.

This invention belongs to the medical field and establishes a clinically relevant mouse glioblastoma treatment model, obtaining "cured" mice. When these "cured" mice were re-challenged with tumors, the tumors spontaneously regressed, and the animals achieved long-term survival. This indicates that the "cured" mice acquired immunity to the tumor, and these mice are named "cured-immune" mice. Intracranial inoculation of tumor cells into the "cured-immune" mice specifically induced a microglia subset exhibiting high expression of the purinergic receptor P2ry12 gene. High ) and immune-boosting functional characteristics, P2ry12 infusion Hi Small glial subsets significantly prolonged the survival time of glioma-bearing mice. Compared with the control group, P2ry12 in "cured-immune" (LTS) mice was significantly reduced. High Ccl12 low Differentially expressed genes in microglial cell subsets are enriched in signaling pathways that promote immune function, hence P2ry12 High Ccl12 low Microglial cell subsets have therapeutic effects on gliomas.

The present disclosure provides techniques for assessing FADS1 activity in a patient. Also provided are techniques for determining the appropriateness of treating a patient with a FADS1-modulating (e.g., inhibiting) compound. This determination can be made by analyzing one or more biological indicators of a FADS1-mediated disease or disorder in the subject. The one or more biological indicators can include one or more of the following in the subject: ratios of polyunsaturated fatty acids ("PUFAs"), the relative abundance of one or more cell types, the relative abundance of one or more differentially expressed genes ("DEGs") (or gene signatures of such DEGs, e.g., RNA), and / or the relative abundance of one or more metabolites. Also disclosed are methods of using FADS1 inhibitors in methods for treating metabolic disorders and obesity.

The invention relates to the technical field of methylation detection site detection, and particularly provides an early gastric cancer lymph node metastasis risk prediction method, system, application and medium, and the method comprises the following steps: obtaining a gastric cancer public data set containing a DNA methylation chip data set and an RNA sequencing data set, and carrying out sample screening, quality control and grouping processing to obtain eight quality control grouping samples; carrying out methylation and RNA difference analysis on the quality control grouped samples to obtain a related gene set of differential methylation sites and differential methylation regions and an RNA differential expression gene set; a gene set of differential methylation sites and an RNA differential expression gene set are integrated and screened to obtain eight target genes. The system comprises a sample acquisition module, a gene analysis module and a gene screening module. Target genes are analyzed and screened on the basis of database biological information, and methylation detection sites for histopathological specimens are screened in combination with lymph node metastasis positive and negative early gastric cancer histological sample verification.

A single-cell level spatial omics multi-modal fusion method, comprising: extracting differential expression genes and nuclear spatial morphological features from spatial transcriptome data, single-cell sequencing data and histological images, and realizing field adaptation between different platforms by using conditional variational autoencoder. Based on a probability inference model, spatial transcriptome expression, single-cell omics and morphological features are fused to jointly infer the type and gene expression level of each cell. A spatial cell network is constructed by a graph attention mechanism to realize the spatial diffusion and recognition of cell types in the whole slice range. Combined with a multi-omics enhancement module, the unmeasured gene and protein expression are completed based on expression similarity, and the prediction consistency is improved through spatial correction. The method realizes high-resolution reconstruction of single-cell multi-omics information in three-dimensional space, improves the information coverage and spatial resolution of spatial omics data, and provides an efficient and low-cost solution for spatial biology and precision medicine research.

The invention discloses a biopsy muscular fascia sample analysis method and system, and relates to the field of muscular fascia sample analysis. The biopsy myofascia sample analysis method comprises the following steps: acquiring pre-stored RNA data of a plurality of biopsy myofascia sample groups; carrying out RNA-seq high-throughput sequencing on the RNA data of each biopsy myofascia sample group to obtain transcriptome data; performing inter-group difference analysis on the transcriptome data according to a preset comparison group, and extracting a differential expression gene data set; and carrying out BDNF layered enrichment analysis on the differential expression gene data set to obtain a BDNF enrichment significant set of each contrast biopsy myofascia sample group, and carrying out cross validation processing based on the BDNF enrichment significant set of each contrast biopsy myofascia sample group. Therefore, it is effectively ensured that the screened BDNF pathway target spot is highly matched with the pathological characteristics of myofascia lesion and is clearly associated with intervention response, and the potential curative effect of acupuncture intervention is accurately evaluated.

The present disclosure provides techniques for accessing FADS1 activity in a patient. Also provided are techniques for determining the appropriateness of treatment of a patient with a FADS1 modulating (e.g., inhibiting) compound. This determination may be made by analyzing one or more biological indicators of FADS1-mediated disease or disorder in the subject. The one or more biological indicators may include one or more of a ratio of polyunsaturated fatty acids (“PUFAs”) in the subject, a relative abundance of one or more cell types, a relative abundance of one or more differentially expressed genes (“DEGs”) (or gene signatures, such as RNA, for such DEGs), and / or a relative abundance of one or more metabolites. Also disclosed are methods of using FADS1 inhibitors in methods of treating metabolic disorders and obesity.

The invention provides a bladder cancer subtype classification system and molecular typing method based on machine learning, and the molecular typing method comprises the steps: firstly obtaining transcriptome data and survival information of bladder cancer tissue of a patient, extracting data from a preset amino acid metabolism related gene set, and constructing a gene expression matrix; then, clustering the patients by adopting an unsupervised clustering algorithm, and determining at least two types of amino acid metabolism molecule subtypes in combination with a stability index; thirdly, carrying out survival difference analysis on different subtypes, screening out differential expression genes related to survival outcomes, constructing a survival prediction model based on the differential expression genes, and calculating amino acid metabolism scores of the patients; finally, the patients are grouped according to the scores, and molecular typing based on the amino acid metabolism characteristics is completed. According to the invention, stable and accurate typing of the bladder cancer patient is realized.

This invention relates to the field of plant disease biological control technology, and particularly to a Fusarium oxysporum cell wall extract, its preparation method, and its application. The extract is derived from the polysaccharide fraction of the cell wall of Fusarium oxysporum spores or hyphae, and contains at least one pathogen-associated molecular pattern (PAMP) component. This component is a non-protein, thermally stable, species-nonspecific elicitor capable of forming an aqueous dispersion system. The extract of this invention can be recognized by plant PRRs and activate PTI immunity. Rehmannia glutinosa tubers treated with the extract showed a significantly increased germination rate after inoculation with Fusarium oxysporum. It also induces superoxide anion production, upregulates the activities of superoxide dismutase, peroxidase, and catalase, and enriches differentially expressed genes in the MAPK signaling pathway, jasmonic acid metabolism pathway, and phenylpropane biosynthesis pathway. Furthermore, the extract can directly inhibit Fusarium oxysporum colony expansion, conidia production, and mycelial growth. Extracts derived from spores or hyphae show no significant difference in inducing disease resistance, and the raw materials are flexible and easy to industrialize. This extract is derived from the cell wall of the pathogen itself, making it environmentally friendly and free from the risk of chemical pesticide residues. It is suitable for the green control of root rot disease in Rehmannia glutinosa.

The present invention belongs to the technical field of bioinformatics, and relates to a machine-learning-based method for screening cross-differentially expressed genes between lung cancer and COVID-19 infection and correspondingly screening prognostic genes of lung cancer. The method comprises: acquiring cross-differentially expressed genes between lung cancer and COVID-19; screening the cross-differentially expressed genes between lung cancer and COVID-19 infection; using Cox regression and LASSO regression to correspondingly screen prognostic genes of lung cancer; and using a K-M survival analysis method, GO and KEGG enrichment analysis methods and a protein-protein interaction analysis method to analyze screened-out prognostic genes of lung cancer. The method provided herein can process data, and can also identify intricate patterns in the data, and has relatively high sensitivity and specificity, thereby improving the efficiency and accuracy of screening. The technique can be extended to the research of other diseases, and features universality.

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 discloses a biomarker prediction system based on multi-omics data integration, a storage medium and a method, and relates to the technical field of data processing and bioinformatics, the system is used for performing gene mutation, CNV difference and transcriptome expression profile analysis on feature data, identifying differential expression genes related to biomarker states, and predicting biomarkers according to the differential expression genes. Obtaining a differential expression gene set; based on the gene set, an XGBoost algorithm is adopted to construct a prediction model, grid search and K-fold cross validation are combined to optimize model parameters so as to improve generalization ability and prevent overfitting, quantitative interpretation is performed on model feature importance by means of an SHAP algorithm, and an optimal key gene feature combination is screened out through multiple training validation to serve as a biomarker prediction panel. The technical effect of automatically discovering small and precise biomarker combinations from complex multi-omics data in a high-stability and high-interpretability mode is achieved, and a transparent and reliable basis is provided for clinical decisions.

The application relates to a screening and determining method of an ischemic cerebral stroke animal model biomarker of qi deficiency and blood stasis syndrome, which integrates proteomics and transcriptomics technologies, systematically screens differentially expressed proteins and genes related to the syndrome, and on the basis, constructs various disease animal models, verifies candidate targets by adopting ELISA, simultaneously combines qPCR technology, multi-dimensionally confirms expression changes in the models, further draws ROC curves, calculates AUC values, sensitivity and specificity and other diagnostic performance indexes, evaluates diagnostic potential, and further knocks down and overexpresses differentially expressed genes, deeply excavates potential biomarkers with diagnostic values, and provides scientific bases for objective diagnosis of ischemic cerebral stroke qi deficiency and blood stasis syndrome and exploration of molecular mechanisms.

The application discloses a plant coding gene and protein with improved cadmium resistance and promoted biomass and a transgenic strain. The application carries out RNA-seq sequencing on switchgrass under cadmium stress treatment, screens a differential expression gene with the highest up-regulation fold, and genome annotation shows that the gene codes a member of a heat shock protein 70 family (Hsp70), a chaperone BiP (Binding Protein) protein, and the gene is named as PvBiP2. An overexpression vector of the gene is constructed and is transferred into a cadmium-sensitive yeast mutant strain (ycf1), and the growth condition of the transgenic yeast on a cadmium-containing culture medium is obviously better than that of a control group. Overexpression of the PvBiP2 gene in switchgrass is found to significantly improve the cadmium resistance of the transgenic switchgrass and promote the biomass of the switchgrass.

The invention provides a method for analyzing and identifying an autophagy biomarker related to atherosclerotic plaque progress, and belongs to the technical field of analysis. Comprising the following steps: (1) data acquisition and processing; (2) identifying differentially expressed genes and autophagy related genes; (3) key gene verification and prediction efficiency evaluation; (4) immune infiltration analysis; (5) single cell RNA sequencing analysis; and (6) expression of the three genes in macrophages. According to the invention, autophagy related genes related to AS are identified, and the expression mode, spatial distribution and immune infiltration characteristics of key differential expression genes are clarified by adopting a multi-omics method. The research result of the invention provides a new insight and potential intervention treatment target for understanding the molecular mechanism of AS.

A clinical decision support system and method for predicting a clinical response of a target tumor to an immune checkpoint blocking therapy (ICB) by: obtaining a training dataset, the training dataset comprising training transcriptome records of tumor tissue samples of a plurality of known responders and a plurality of known non-responders to the ICB; deconvolution is carried out on the training data set to identify differential expression genes DEG in the training data set, the DEG is regarded as features of the training data set, and related responder and non-responder states are regarded as labels recorded in the training data set; performing regression analysis on the features to select a feature subset which can predict the responder tag or the non-responder tag and related feature weights; incorporating the feature subset and the associated feature weight into a reaction estimation model; receiving transcriptome data of the target tumor tissue sample; the transcriptome data is processed using the generated response estimation model to generate an estimated response indicator indicative of a possible response of the target tumor to the ICB.

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.