745 results about "Bio informatics" patented technology

The invention belongs to the field of bioinformatics, and relates to a molecular property prediction method based on multi-modal gating and comparative learning, which comprises the technologies of comparative learning, graph neural network, cross-modal alignment, gating attention and the like. Firstly, data standardization and graph construction are carried out, and molecular fingerprint embedding is extracted; secondly, a heterogeneous dual-channel graph coding architecture is adopted, one path captures atom short-range interaction through an attention mechanism, the other path integrates a molecular global structure and long-range dependence, and complementary molecular representation is generated; then, a cross-modal attention mechanism is introduced, bidirectional association of graph and fingerprint features is achieved, and modal weights are adaptively and dynamically distributed through a gating fusion module; and finally, a comparison pre-training strategy is adopted, a molecular graph and fingerprints are utilized to construct a sample pair, and discriminative molecular representation is learned on unlabeled data. According to the method, the accuracy of molecular property prediction is remarkably improved, and an efficient and reliable calculation tool is provided for virtual drug screening and lead compound optimization.

The invention discloses a metabonomics data batch correction method based on multi-kernel learning, and belongs to the cross technical field of bioinformatics and analytical chemistry. According to the method, the multi-kernel learning technology is utilized, the advantages of different kernel functions are fused in a self-adaptive mode, a model conforming to data reality is constructed, complex drift characteristics of metabolite signals are accurately captured, and efficient and accurate normalization processing of metabonomics data is achieved. Compared with traditional data standardization methods such as SVR and LOESS, the method has the advantages that the performance is excellent in the aspect of reducing the metabolite peak intensity variability, and the data stability is remarkably improved. In the subsequent multivariate statistical analysis, the classification accuracy is greatly improved, the comparability among different batches of data is also remarkably enhanced, reliable data support can be provided for discovery of disease biomarkers, and the method plays a key role in large-scale metabonomics research.

The invention belongs to the field of bioinformatics, and relates to an improved integrated deep learning cell communication ligand-receptor interaction prediction method. The method comprises the following steps: firstly, carrying out extraction and dimensionality reduction on biological sequence features of a ligand and a receptor, and constructing multi-modal feature input; secondly, constructing an improved deep neural network branch, introducing a batch normalization layer and a Leaky ReLU activation function, solving the problems of gradient disappearance and neuronal necrosis, and improving regularization strength to prevent overfitting; meanwhile, an enhanced heterogeneous graph auto-encoder branch is constructed, the graph embedding dimension is remarkably expanded to improve the feature capacity, and full convergence of the model is ensured by increasing training rounds; thirdly, fusing the improved deep network with the prediction probability of a heterogeneous graph auto-encoder by adopting a weighted integration strategy; and finally, outputting a potential interaction relationship based on the fusion probability. By optimizing the architecture and the strategy, the prediction accuracy and robustness are remarkably improved, and a reliable tool is provided for analyzing a complex cell communication network.

The invention belongs to the field of bioinformatics, and relates to an anti-cancer drug response prediction method based on an attention mechanism. The method comprises the following steps: firstly, capturing uniform-dimension drug and cancer cell line characteristics through a multi-layer perceptron; secondly, fusing drug characteristics by adopting a Transform encoder, and constructing a cell encoder for cancer cell line characteristic polymerization; then, designing a cross-modal cross fusion module to promote information interaction between the two; and finally, predicting a semi-suppressed concentration value subjected to logarithmic transformation between the two through a multi-layer perceptron. Experimental results show that compared with an existing optimal method, the method has the advantage that the RMSE is reduced by 2.9%. According to the method, accurate prediction of the anti-cancer drug response is achieved by integrating drug and cancer cell line data, screening of potential anti-cancer drugs can be accelerated, personalized treatment schemes can be optimized, the cure rate of cancer patients is further increased, and the method has great significance in cancer treatment.

The invention provides a method and system for predicting the utilization rate of feed amino acid in multiple stages of laying hens, and relates to the field of bioinformatics, and the method comprises the following steps: obtaining chemical component measured values of feed raw material samples in different growth stages of the laying hens and in-vivo measured values of standard ileum amino acid digestibility; performing predictive factor screening according to the chemical component measured value and the standard ileum amino acid digestibility in-vivo measured value to obtain a predictive factor combination; constructing a prediction model according to the prediction factor combination to obtain a candidate prediction equation; optimizing according to the candidate prediction equation, and screening to obtain an optimized equation; performing model verification according to the optimization equation to obtain a target prediction model; and performing digestibility prediction according to the target prediction model to obtain a predicted standard ileum amino acid digestibility value. According to the method, the standard ileum amino acid digestibility is accurately predicted based on in-vitro detection data, and the limitation of a traditional in-vivo determination method is effectively overcome.

The invention discloses a method for evaluating enhancement and release seedling resources based on environmental DNA polymerization analysis. The method comprises the following steps: carrying out gridding partition on a target water area, collecting a water sample through a designed sampling scheme, and carrying out DNA extraction and high-throughput sequencing to obtain species sequence information of each sampling point. Sequencing data is subjected to species identification by using a bioinformatics method, released species are identified, a spatial abundance model is established, and a preliminary distribution map is generated. And establishing a DNA degradation kinetic model in combination with water area environmental parameters, and carrying out reverse correction on abundance distribution. And through a resource inversion model coupled with hydrodynamics, analyzing biomass distribution characteristics and migration laws of the release group, and obtaining a resource evaluation result. And finally, a species environment preference model is constructed based on migration path analysis, an optimal release area is matched in a target water area, a scientific scheme including release point locations, opportunities and quantity is generated, and a whole-process technical support and a decision basis are provided for enhancement and release.

The invention discloses a method and system for predicting juvenile depression based on intestinal flora, and relates to the technical field of bioinformatics and artificial intelligence, and the method comprises the steps: firstly, obtaining an original sequence of a microbiome, carrying out the preprocessing of the original sequence of the microbiome, and obtaining a feature matrix; and screening core flora characteristics with stable trans-folding by adopting characteristic importance evaluation and interpretability analysis based on a gradient boosting decision tree. A mixed weighted graph is constructed based on Spearman correlation and a proximity relationship, and an absolute value of a correlation coefficient is taken as an edge weight and an edge density is adjusted through a threshold adaptive strategy. And finally, through an improved graph attention neural network, based on edge weight attention, layer normalization and random inactivation, enhancing robustness, and adopting adaptive optimization to complete parameter learning. And determining a dynamic classification threshold according to the AUC of the target patient, and outputting a sample discrimination result and confidence. According to the method, the accuracy, stability and biological interpretability of juvenile depression recognition are remarkably improved.

The invention discloses a protein palmitoyl transferase prediction method and system based on a multi-branch deep convolutional neural network, and belongs to the technical field of bioinformatics and artificial intelligence. The method comprises the following steps: S1, obtaining a to-be-detected protein sequence; s2, inputting the protein sequence into a pre-trained iPalmT model; and S3, judging whether the target protein is palmitoyl transferase or not according to a model output result. The iPalmT model comprises a coding module, two paths of parallel convolution branches, a feature fusion module and a classification module; and after the convolution layers of each convolution branch are stacked, an SE module is arranged and is used for channel weighting and feature re-calibration. The model extracts multi-level sequence features through convolution kernels of different scales, realizes high-precision prediction through feature fusion and a residual structure, can automatically learn multi-scale features from large-scale data, realizes end-to-end palmitoyl transferase recognition, and has high accuracy and good universality.

The invention belongs to the technical field of bioinformatics, particularly relates to a computer simulation and regulation method and system for beef cattle fat metabolism, and aims to solve the problems of excessive fat deposition, low feed conversion efficiency, difficulty in multi-objective optimization and the like in existing beef cattle breeding. A multi-scale biological network model is constructed, a computer simulation and prediction engine is developed, an intelligent intervention strategy optimization engine is designed, and implementation and feedback optimization are carried out. By reducing unnecessary fat deposition, improving the feed conversion efficiency and improving the beef quality, the economic benefit of beef cattle breeding is remarkably improved, the requirements of consumers for high-quality and safe beef products are met, and the method has important value for promoting intelligent upgrading and sustainable development of the beef cattle industry.

The invention discloses a tumor personalized drug recommendation method and system based on fusion of multiple clinical guidelines, and belongs to the technical field of bioinformatics and precision medicine. The method comprises the following steps: acquiring gene variation data and clinical feature information of a patient; generating a preliminary drug candidate list based on drug recommendation rules of a plurality of clinical guidelines; calculating the weight of each guide by adopting a dynamic weight distribution algorithm; obtaining drug-gene-disease associated information through multi-hop reasoning of the knowledge graph; calculating a drug evidence score by adopting a multi-dimensional scoring algorithm; carrying out personalized score adjustment in combination with individual features of the patient; and outputting a personalized drug recommendation result. According to the method, through multi-guide dynamic fusion, multi-dimensional evidence scoring, knowledge graph reasoning and personalized adjustment, the problems of incomplete guide coverage, lack of personalization, low response speed and the like in the prior art are solved, the method has the advantages of high accuracy, high clinical applicability, quick response and the like, and the clinical real-time decision-making requirement can be met.

The invention relates to the technical field of bioinformatics and clinical medicine, and discloses a multi-omics data association analysis-based tumor early screening and typing early warning system, which comprises a data acquisition and preprocessing module for integrating standardized longitudinal multi-omics data; the dynamics and topology analysis module is used for generating topology fingerprints representing dynamic behaviors of the system through state space reconstruction and persistent coherence analysis; the causal inference and risk assessment module is used for calculating critical moderation indexes in parallel to synthesize risk indexes and constructing a dynamic causal network; and a collaborative diagnosis and report generation module. According to the system, risk indexes derived by critical moderation, topological fingerprints and a dynamic causal network are creatively combined, multi-modal information fusion is carried out through a collaborative diagnosis unit, and finally a comprehensive early warning report is generated. According to the invention, the accuracy and reliability of early risk early warning of tumors can be obviously improved, and a mechanism-level traceability basis is provided for clinical intervention.

The invention discloses a chicken manure crude protein content detection model construction method, a chicken manure crude protein content detection system and a detection method, relates to the technical field of bioinformatics, solves the problems of high cost, serious pollution and low efficiency of an existing chicken manure crude protein content detection technology, and improves the accuracy of chicken manure resource application. The obtained sample set comprises visible-near infrared reflection spectrum and crude protein content of the chicken manure; carrying out pretreatment on the visible-near infrared reflection spectrum by adopting a plurality of pretreatment methods; screening out an optimal preprocessing method and a corresponding preprocessing spectrum by using a partial least square regression model; obtaining a characteristic wave band by using a plurality of characteristic selection methods; and based on the crude protein content and the characteristic wave band, constructing and screening an optimal chicken manure crude protein content detection model. The method provided by the invention is suitable for detecting the content of crude proteins in chicken manure by using manure resources.

The invention discloses a chicken manure crude protein content detection model construction method, a chicken manure crude protein content detection system and a detection method, relates to the technical field of bioinformatics, and solves the problems that an existing chicken manure crude protein content detection technology is serious in pollution in the detection process, and a spectral analysis technology is serious in information redundancy during detection, high in cost and poor in model stability. The sample set comprises visible-near infrared reflection spectrum and crude protein content of chicken manure; obtaining three spectral indexes corresponding to the visible-near infrared reflection spectrum, the MSC pretreatment spectrum and the SNV pretreatment spectrum, and screening out the optimal wavelength position and the corresponding three spectral indexes; constructing a data set by using the three spectral indexes and the crude protein content, and performing fitting through multiple fitting modes; evaluating the plurality of fitting equations, and screening out an optimal fitting equation and a corresponding pretreatment method and wavelength position as a chicken manure crude protein content detection model. The method provided by the invention is suitable for detecting the content of crude proteins in chicken manure by using manure resources.

The invention relates to the technical field of big data management, in particular to a bioinformatics data classification method and system based on big data, and the method comprises the following steps: obtaining time sequence recognition trend reversal and positioning fragments, extracting recognition difference positions inside and outside a frequency band data division region, screening samples with consistent features, and rearranging path labels; connecting nodes are cut off to generate fracture indexes, and label states are updated and written into sample fields to form a classification result set. According to the method, a labeling area is constructed by extracting trend inversion points in a time sequence, sample fragments are divided by combining data fluctuation positions in a disturbance frequency band, label numbers are arranged according to the fluctuation sequence of samples in a path, a corresponding sequence of a label chain connection relation and the sample positions is established, and label section boundaries are positioned and limited by fracture nodes. And the updated label state is synchronously written into a sample field, and the path label is bound according to a chain sequence, so that the sample identifier is kept coherent in sequence change, and the continuous coverage capability of the path information in classified output is improved.

The invention discloses an automatic construction system for a biological information analysis process, and the system comprises an intention understanding and semantic analysis module which is used for analyzing a natural language text inputted by a user into a structured task description meeting the requirements of a biological information analysis task; the knowledge graph and retrieval module is responsible for constructing a knowledge graph special for the bioinformatics field so as to provide knowledge retrieval and recommendation services; the process generation core module is used for receiving the structured task description, actively associating the knowledge graph with the retrieval module so as to supplement the field large model, and generating a biological information analysis target process language code; and the execution and monitoring module is used for guaranteeing workflow execution, full-life-cycle state monitoring, real-time fault diagnosis and intelligent self-healing decision making of target process language codes. According to the system, the executable analysis process can be directly generated according to the natural language requirement of the user, the tool compatibility and parameter validity are verified through the borrowed knowledge graph before execution, the dependency on the programming ability of the user is greatly reduced, and the process operation reliability is improved.

The invention provides a chitin synthetase 2 gene of diplodia juncoides and application of the chitin synthetase 2 gene, dsRNA for preventing and treating the diplodia juncoides and application of the dsRNA, and relates to the technical field of biology. According to the invention, through a bioinformatics technology, the Hypochloa diplosa CHS2 gene is determined, the growth of the Hypochloa diplosa can be effectively inhibited by silencing the gene, and the prevention and control of the Hypochloa diplosa can be realized. The dsRNA designed and obtained based on the gene can mediate silencing of the CHS2 gene of the diplodia juncosa, and specifically realizes prevention and control of the diplodia juncosa; poisoning of chemical pesticides to non-target beneficial insects is avoided; the use of chemical pesticides is reduced, and the pollution to the environment is reduced.

A complex bait data set construction method based on structure and sequence collaborative redundancy elimination belongs to the field of bioinformatics, and comprises the following steps: screening an initial protein complex structure set, removing entries containing nucleic acids, small molecules or non-protein chains, and selecting binary complexes meeting integrity and resolution requirements; secondly, structure clustering and sequence clustering are carried out based on three-dimensional structure similarity and sequence homology, combined comparison is carried out on the two results, and redundant compound entries which are highly similar in structure and sequence are removed; then, taking each cluster representative compound as a target, generating a plurality of groups of bait structures by using a molecular docking or prediction modeling method, and calculating a quality index; and finally, performing stratified sampling and proportion balance based on the score interval of the quality index, and constructing a high-quality protein complex bait data set with structure and sequence collaborative redundancy elimination and balanced quality distribution. The data set generated by the method has the advantages of low redundancy, high diversity and quality distribution controllability.

The invention discloses a method and system for screening drug targets with space-time specificity and computer equipment, and relates to the technical field of bioinformatics and computational biology. The screening method is based on single cell transcriptome sequencing data, and comprises the following steps: (1) quantitatively reconstructing spatial positioning and functional modes of cells in tissues, namely 1.1) carrying out data preprocessing on the single cell transcriptome sequencing data; 1.2) reconstructing the spatial positioning of the single cell; 1.3) reconstructing a single cell biological function mode; (2) screening a drug target with space-time specificity, wherein the screening comprises the following steps: 2.1) cell-cell communication analysis; the invention discloses a single-cell data analysis method based on a GRN (Gene Regulatory Network), which is characterized by comprising the following steps of (1) establishing a single-cell data analysis method, (2) establishing a GRN (Gene Regulatory Network) taking a specific tissue microenvironment state as a core, and (3) discovering a target spot. The single-cell data analysis method is innovative, provides a new thought and a technical path for research and development of drugs for metabolic diseases and other systemic diseases, and has a popularization and application basis.

The invention discloses a 26 Multi-InDel genetic marker system for highly degraded sample typing, a detection primer and application of the 26 Multi-InDel genetic marker system, and belongs to the technical field of forensic medicine identification. According to the invention, through bioinformatics screening and experimental verification, a multi-InDel genetic marker system of which the lengths of 26 amplicons do not exceed 125 bp is constructed. The problem that degradation detection material detection and high system efficiency are difficult to consider at the same time is solved. The random matching probability of the system in Han population in Hunan reaches 2.25 * 10 <-17 >, the cumulative paternity exclusion rate is 0.999925, complete typing of highly degraded DNA can be achieved, triad paternity test can be completed, and a new technical scheme which is efficient and compatible with a capillary electrophoresis platform is provided for forensic practice.

The invention provides an antibody drug conjugate property prediction method based on multi-modal fusion, and belongs to the field of bioinformatics. The method comprises the following steps: firstly, explicitly modeling sequence position information through sine position coding; secondly, introducing a bidirectional cross attention mechanism to establish interaction between a light chain and a heavy chain and alignment between an antigen and an antibody; thirdly, the integrated graph neural network reconstructs the adjacency relation according to the attention weight, and topological features are extracted; and finally, in combination with a double-stage self-adaptive refining module, two-stage treatment of alignment and refining is realized, and each modal feature contribution is adjusted in a self-adaptive manner. And meanwhile, the sequence robustness is improved through Mask perception feature extraction, and the interpretability analysis of the key binding sites is realized through the attention weight. The method can significantly improve the prediction accuracy, and can be widely applied to cancer targeted therapy and drug design optimization.

The invention belongs to the field of bioinformatics, and relates to a protein post-translational modification prediction method based on multi-modal deep learning. The method comprises the following steps: firstly, performing multi-modal feature extraction by inputting a protein sequence and three-dimensional structure data to obtain a sequence feature vector and a structure feature vector; secondly, carrying out feature fusion by adopting a cross-modal attention mechanism and a self-adaptive gating network; then, combining the fusion features with the disease type information, and performing fine adjustment on the prediction probability through a disease specific coding network; then, using a multi-task learning framework to predict the locus probabilities of various protein post-translational modification types in parallel; finally, feature importance is calculated through a gradient back propagation technology, and a comprehensive report is output in combination with variation influence analysis. According to the method, high-precision and explainable protein post-translational modification prediction with disease perception capability is realized, and an important calculation and analysis tool is provided for revealing a disease molecular mechanism and finding accurate drug targets.

The invention discloses an esophageal cancer prognosis risk analysis method and system based on machine learning, and a medium, and relates to the technical field of artificial intelligence technology and bioinformatics, and the method comprises the steps: 1, collecting multi-modal data of a patient, and carrying out the standardization processing, so as to construct a stable feature set; 2, constructing a plurality of machine learning models, realizing high and low risk group prediction of each machine learning model based on stable feature set modeling, and determining the machine learning model and an optimal feature set according to the high and low risk group prediction; and step 3, calculating SHAP interaction values among the features in the optimal feature set, drawing an interaction value curve according to the SHAP interaction values to obtain TopA interaction feature pairs, taking the interaction feature pairs as newly constructed features to be included in the original feature set in the step 1, and repeating the feature screening process in the step 1 and the step 2 to obtain an optimal machine learning model. Predicting high and low risk groups of patients; according to the prognosis risk analysis method, the detection specificity of high and low risk groups of local advanced esophageal cancer patients is greatly improved.

The invention belongs to the technical field of bioinformatics, and discloses a protein disulfide bond mutation site prediction method and device, equipment and a storage medium, a plurality of candidate residue pairs are extracted by obtaining a prediction structure of a to-be-modified protein, and structural feature information of each candidate residue pair is extracted; comprising an inter-residue distance matrix of two residues, a sequence distance, a relative solvent accessible surface area of each residue, a depth index and a predicted local distance difference test value, inputting a prediction model to obtain a probability value of forming a disulfide bond by each candidate residue pair, determining the candidate residue pair with a probability greater than a specified probability as a disulfide bond mutation site, therefore, the bonding probability of the disulfide bond can be comprehensively predicted by using more comprehensive features, the high dependence of the model on individual traditional features is reduced, the high sensitivity of a traditional method on atomic coordinates and the uncertainty of a prediction result are overcome to a certain extent, and the method has higher robustness and is suitable for popularization and application. The prediction accuracy of the protein disulfide bond mutation site is improved.

The invention discloses a multi-modal feature integrated risk assessment method and system for a stroke dangerous group, and relates to the technical field of telemedicine collaboration, a telemedicine collaboration network platform is built, and multi-modal data of a stroke high-risk group is collected and processed; analyzing the relation between risk factors and a cerebral apoplexy pathological mechanism by applying bioinformatics and medical knowledge, and defining a key action path; and based on an analysis result of the key action path, integrating multi-modal data by taking a pathological mechanism as an axis, and constructing a dynamic risk knowledge network based on a knowledge graph. According to the method, multi-modal data are integrated, a comprehensive patient individual feature matrix is constructed, a dynamic risk knowledge network is combined, and the association between patient individual features and a cerebral apoplexy pathological mechanism and the dynamic change of risk factors are accurately analyzed, so that an accurate risk score is calculated, the cerebral apoplexy risk of a cerebral apoplexy risk crowd is analyzed, and the cerebral apoplexy risk of the cerebral apoplexy risk crowd is analyzed. A powerful basis is provided for prevention of the cerebral apoplexy, and the occurrence rate of the cerebral apoplexy is reduced.

The invention belongs to the technical field of artificial intelligence algorithm and bioinformatics crossing, and relates to a drug interaction prediction method based on multi-modal molecular characterization. According to the method, through the edge perception GCNII architecture and the Hop2Token multi-hop coding mechanism, effective modeling of atomic-level and bond-level local environments and a cross-substructure high-order dependency relationship in drug molecules is realized, and the accuracy and robustness of drug interaction prediction are improved. According to the method, Mol2Vec and MolT5 cross-modal molecular characterization is integrated, fusion of molecular overall semantics and substructure grammar semantic association is achieved, and the generalization ability of the model to complex molecules and unknown medicine combinations is remarkably improved. According to the method, the dynamic feature screening algorithm driven by the SHAP value of the artificial intelligence technology is adopted for biological verification, the feature redundancy problem is effectively solved, the molecular biological information analysis processing calculation efficiency and the model transparency are improved, and the traceability of the prediction process is guaranteed.

The invention discloses a neural network calculation method and device for gene expression regulation and control analysis, and relates to the technical field of bioinformatics, and the method comprises the steps: obtaining first feature data and second feature data; constructing an input feature comprising a plurality of regulation and control hierarchies; and inputting the input features of the plurality of regulation levels and the second feature data into the target neural network model, and outputting a prediction result of the gene expression state. According to the neural network calculation method provided by the invention, chromatin accessibility and three-dimensional space interaction data are deeply fused through a dynamic routing module, so that the problem of'black box 'which is inaccurate in prediction and difficult to explain in a traditional deep learning model is solved in a mode of explicitly simulating a real biological regulation mechanism; and a key gene regulatory pathway can be accurately identified.

The invention relates to the technical field of crossing of bioinformatics, artificial intelligence and plant genomics, and discloses a plant single cell gene expression prediction method, system, device and medium. A plant single cell gene expression prediction model realizes dynamic feature fusion of a DNA sequence and chromatin accessibility signals through a gated cross attention mechanism; the problem that a traditional single-mode model cannot model regulation and control dynamic association is effectively solved, and the result interpretability is enhanced; a hybrid expert system and a load balancing design are adopted to significantly improve the recognition capability of the model for rare cell types, and a decoupling prediction head design supports efficient transfer learning; a DNA long sequence processing mechanism and nucleosome scale feature coding ensure cross-species compatibility; an end-to-end automatic process and a dynamic parameter optimization framework greatly improve the practicability; a'prediction-verification 'closed-loop support system can be constructed for molecular breeding, and high-precision and interpretable prediction of plant single-cell gene expression is realized.

Compositions, methods, kits, systems, and software are provided for non-invasive prenatal testing for autosomal recessive diseases. Next generation sequencing is used to sequence maternal and fetal DNA isolated from maternal plasma by probe capture. The fetal fraction of the sequencing reads for DNA isolated from maternal plasma is estimated by counting single nucleotide polymorphisms (SNPs) for which an allele is detected that is present in the paternal haplotype but absent in the maternal haplotype, based on the assumption that SNPs having a paternal allele belong to the fetal DNA. The fetal fraction is bioinformatically enriched by excluding sequencing reads over a specified length via in-silico size selection, which increases fetal genotype prediction accuracy. Parental haplotype information together with the read ratios observed at the linked SNPs is used to predict the fetal genotype at a site of a mutation linked to the autosomal recessive disease.

This invention relates to the field of medical testing, specifically to a set of biomarkers, reagent kits, and their applications for diagnosing hypertension in children. This invention involves collecting tongue / intestinal samples from obese children with hypertension, obese children, and healthy individuals, performing metagenomic sequencing, and statistically analyzing the sequencing data using bioinformatics to identify disease-related tongue / intestinal flora. By integrating tongue / intestinal flora with disease information, a combination of flora biomarkers is obtained. A binary classification prediction model constructed using this combination can maximally detect hypertension in obese children.