67 results about "Gene regulatory network" patented technology

The invention provides a cell specific transcription factor regulatory network analysis method and a visualization platform, and relates to the technical field of bioinformatics, the method comprises the following steps: constructing a gene regulatory network through a GRNBoost2-cisTarget-AUCell-Cell GRN workflow based on a transcription factor in combination with a motif database; screening a direct regulation relationship in combination with the database, and calculating an activity score of a regulator in each cell; based on activity scores and cell type annotation results, grouping the single cell data by using a unified manifold approximation and projection (UMAP) dimensionality reduction method and a Leiden clustering algorithm, and displaying the following results through an interactive visualization tool: a cell clustering UMAP graph, performing color marking according to cell types; a UMAP graph and a heat map of transcription factor regulator activity; according to the visual map of the gene regulation and control network, transcription factors and target genes are distinguished through node shapes, and regulation and control relations are marked through line weights and colors. According to the invention, an accurate regulation and control network can be provided.

Disclosed herein are methods of increasing nitrogen fixation in a non-leguminous plant. The methods can comprise exposing the plant to a plurality of bacteria. Each member of the plurality comprises one or more genetic variations introduced into one or more genes or non-coding polynucleotides of the bacteria's nitrogen fixation or assimilation genetic regulatory network, such that the bacteria are capable of fixing atmospheric nitrogen in the presence of exogenous nitrogen. The bacteria are not intergeneric microorganisms. Additionally, the bacteria, in planta, produce 1% or more of the fixed nitrogen in the plant.

The invention provides a cell development process dynamic modeling method and device based on time sequence single cell transcriptome data and a medium, and relates to the crossing field of bioinformatics and computational biology. The method comprises the following steps: constructing a Shenchang differential equation learning framework; adjusting parameters of the single cell development state change model based on the Shenxuan differential equation learning framework so as to construct a population cell development state change model; obtaining a cell specific gene regulation network and a population cell gene regulation network based on the population cell development state change model so as to predict occurrence opportunity of cell lineage differentiation and a molecular decision mechanism of cell differentiation; therefore, the problems of incomplete modeling mechanism, insufficient noise processing and lack of energy principle in the existing cell development process are solved.

The invention discloses an inference method and an inference system of a gene regulatory network. The inference method comprises the following steps: acquiring transcriptome data and prior gene network data of a single cell; extracting a gene sub-network related to the transcriptome data from the prior gene network data; obtaining a first view and a second view for the gene sub-network according to a first random deletion strategy and a second random deletion strategy; providing the first view and the second view to a neural network model, and performing comparative learning based on the neural network model to obtain incoming features and outgoing features of each node in the gene sub-network; and constructing a regulation score matrix based on the incoming features and the outgoing features, wherein the regulation score matrix displays the regulation association degree between genes in the transcriptome data of the single cell. According to the technical scheme, the direct causal relationship and the indirect association relationship can be effectively distinguished, so that the gene co-expression network more accurately reflects the real regulation relationship.

A gene regulation inference method based on causal diagram embedding and conditional cellular network relates to the technical field of gene regulation network prediction, and comprises the following steps: 1, obtaining a gene expression matrix from scRNA-seq, and generating a causal diagram; 2, generating a local feature embedding matrix of a gene by using a graph neural network model based on a causal graph and a known gene regulation and control network graph; 3, constructing a CCSN based on scRNA-seq, converting the CCSN into gene connectivity vectors, and integrating the gene connectivity vectors of all cells to form a CNDM as a global feature matrix; 4, integrating the local feature embedding matrix and the global feature matrix to form a final gene feature matrix; 5, screening a core gene from the gene feature matrix, and constructing a regulation edge matrix; and 6, inputting the regulatory edge matrix into a gene link prediction module to realize inference of the gene regulatory network. By applying the method, the causal relationship and the cell specificity can be integrated, the core gene is effectively screened, the feature fusion is optimized, and the accuracy and the biological interpretation of network inference are improved.

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 embodiment of the invention provides a cluster collaborative navigation method, a control system and a storage medium. The method comprises but is not limited to the technical field of navigation. The method comprises the following steps: in an upper layer module of a gene regulation and control network, determining a form boundary curve according to first position information of first execution equipment, second position information of second execution equipment and third position information of an obstacle; in a lower layer module of the gene regulation and control network, determining a tangential propulsion speed component and an offset correction speed component according to the form boundary curve and the first position information; and in a lower layer module of the gene regulation and control network, according to a visual adjacent distance regulation speed component, a tangential propulsion speed component and an offset correction speed component of the visual projection field, determining a target linear speed and an angle parameter. According to the embodiment of the invention, collaborative navigation and dynamic form maintenance of the cluster system can be realized.

The invention discloses a differential gene regulation and control network reconstruction method based on mutual information and a redundancy regulation and control filtering system. The method comprises the following steps: firstly, collecting gene expression data under two or more conditions, respectively calculating mutual information of gene pairs under each condition, and screening the gene pairs with obvious mutual information difference as candidate regulation edges; furthermore, a redundancy regulation and control relation caused by the intermediary variables is identified and eliminated through calculation condition mutual information, and finally a difference regulation and control network with high credibility is constructed. The system comprises a data preprocessing module, a mutual information calculation module, a redundancy indirect regulation and control effect filtering module, a regulation and control direction judgment module, a network construction and threshold optimization module, a difference network construction module and an output and visualization module. The method can effectively improve the biological interpretability and inference accuracy of the network, and is widely applied to the bioinformatics fields such as disease mechanism research, regulatory factor identification and multi-omics integrated analysis.

The invention discloses a pig SNP chip construction method based on gene regulatory network characteristics, and relates to the technical field of animal genetic breeding, and the pig SNP chip construction method comprises the following steps: constructing a pig tissue specificity multi-level gene regulatory network related to target traits and tissue types; performing function annotation and network comprehensive feature extraction on the whole genome SNP based on the gene regulation network; and carrying out score sorting and screening on the extracted network comprehensive characteristics, screening SNP variation sites with regulation function potential, constructing a pig SNP site set, and preparing the SNP chip. By introducing tissue / character related gene regulatory network information, functional priority ranking and screening are performed on SNP loci in a whole genome range, so that the character interpretation ability and breeding value estimation accuracy of SNP in a chip are remarkably improved.

The invention discloses a method and a device for identifying a transcription factor of a tissue gene, relates to the technical field of biological information, and mainly aims to solve the problem of poor identification accuracy of the transcription factor of the existing tissue gene. Comprising the following steps: acquiring an initial gene expression matrix of tissue genes, and constructing a full-connection directed graph based on the initial gene expression matrix; after the full-connection directed graph is converted into an undirected role graph, the undirected role graph and a feature matrix are reconstructed based on a graph neural network model after model training is completed, a reconstructed target gene expression matrix is obtained, and a loss value of the graph neural network model is obtained through calculation after edges are dynamically removed; and generating a directed gene regulatory network based on the target gene expression matrix, and identifying a transcription factor of the tissue gene based on the directed gene regulatory network.

The application discloses a gene regulation network inference method combining information theory and machine learning, including obtaining time series of different gene expression processes, converting the time series into symbol sequences, calculating the symbol transition entropy between different gene symbol sequences, calculating the regulation gene set of each gene, constructing a model for the time series of a target gene and the time series set corresponding to the regulation gene set of the target gene and training the model, calculating the importance score of the regulation gene, screening the regulation gene with the importance score meeting a first threshold value and adding the regulation gene into a core regulation gene set; obtaining the symbol transition entropy of all core regulation genes to the target gene, combining the importance score and the symbol transition entropy of the core regulation gene into a regulation coefficient after normalization, screening the core regulation gene set meeting a second threshold value, and obtaining the core regulation gene set of all target genes. The method reduces the calculation complexity, solves the overfitting problem of the model based on machine learning, and improves the prediction accuracy.

The invention belongs to the technical field of biomedical engineering, and discloses a gene regulatory network optimization method based on a diffusion model, which comprises the following steps: acquiring gene data of cells under a steady state condition, and constructing a gene expression matrix according to the gene data; injecting Gaussian noise into the gene expression matrix based on a diffusion model method to generate a series of noisy data sequences; performing noise estimation and structure estimation on the noisy data sequence by a noise estimator and a structure estimator based on a gene regulation and control network, and performing reverse denoising processing according to the noise estimation and the structure estimation to obtain gene structure estimation after reverse denoising; performing structure optimization on the gene structure estimation after reverse denoising by adopting an acyclic constraint function and a regularization substitution method; and outputting the optimized gene structure estimation. According to the method, the regulation and control relation between the genes is accurately recognized from high-dimensional gene expression data, and the modeling precision of the regulation and control relation between the genes is improved.

The invention discloses a gene regulatory network prediction method and system based on explicit correlation modeling, and the method comprises the steps: obtaining a gene expression matrix of single-cell RNA sequencing data and an adjacent matrix of a prior regulatory graph constructed based on prior knowledge, and inputting the matrixes into a graph neural network model; wherein the graph neural network model is configured to perform explicit modeling on a link in the prior regulation and control graph through an intra-layer message passing space and an inter-layer message passing space so as to obtain link representation; predicting whether a regulation relation exists between the gene pairs through a classifier on the basis of link characterization so as to deduce a gene regulation network; wherein the architecture of the graph neural network model is adaptively determined through an automatic architecture search algorithm according to input data. According to the framework provided by the scheme, modeling link representation is displayed in the message passing process, the regulation and control relation of the gene pair is inferred by MLP based on link embedding, utilization and organization of complex connection information of the prior regulation and control graph are enhanced from the source, and the inference accuracy is effectively improved.

The invention discloses a gene regulation and control network prediction method based on line graph attention and Transform. The method comprises the following steps: firstly, acquiring a gene expression matrix and a gene regulation prior network, and preprocessing the gene expression matrix; then, selecting gene nodes from the gene regulation prior network, and constructing a closed subgraph by taking a target gene node pair as a center; converting the closed sub-graph into a line graph, and constructing a line graph node feature matrix; finally, a gene regulation and control network prediction model is constructed, and the model comprises a graph attention neural network, a Tannformer encoder and a multi-layer perceptron; the graph attention neural network extracts gene local spatial features by using the line graph and the line graph node feature matrix; the Tannform encoder extracts global spatial features of the gene by using the gene expression matrix; and splicing the gene local spatial features and the gene global spatial features, and predicting regulation edges of the spliced features through a multi-layer perceptron. According to the method, the key regulation relation can be distinguished more effectively in the gene regulation network with high noise and sparse structure.

This invention discloses a gene regulation inference method guided by topological data analysis and gene network embedding. It combines TDA and GNN to enhance the inference capability of gene regulation networks. By capturing the topological structure of the gene regulation network graph through TDA features, the model's ability to model gene expression is enhanced. The TDA features and GAT embedding representations are effectively integrated through gating fusion. This fusion mechanism enables the model to adaptively adjust node embeddings based on global topological characteristics, which not only improves the accuracy of gene interaction representation but may also enhance the accuracy of regulatory relationship prediction. The traditional GAT architecture is extended through a four-layer graph attention mechanism. Each layer uses residual connections to alleviate the gradient vanishing problem and improve training stability. In addition, independent multilayer perceptron branches are designed for transcription factors and target gene embeddings. This deep architecture can achieve more expressive feature transformations and capture subtle patterns in gene regulation networks.

The application discloses a kind of based on deep directed graph convolution gene network regulation relationship reconstruction method, comprising:1, using Node2Vec method obtains the topological information of Saccharomyces cerevisiae gene regulation network;2, for Saccharomyces cerevisiae factor regulation network using graph data enhancement means, obtain more efficient information expression;3, for the regulation direction problem of Saccharomyces cerevisiae gene regulation network, using directed graph neural network to predict the regulation relationship direction information;4, construct deep directed graph convolution model, obtain the neighbor information of gene high order;5, the expression characteristics of Saccharomyces cerevisiae gene and the characteristics obtained in steps 1, 2 are spliced and then input into model to obtain Saccharomyces cerevisiae regulation network prediction score matrix, according to the relationship between prediction score matrix and threshold value, judge whether there is regulation relationship between genes.The application can accurately predict the regulation relationship between Saccharomyces cerevisiae genes, and help to more efficiently study the nature of biochemical reaction of organism.

The present invention relates to the field of data analysis technology, and in particular to a method for analyzing cell differentiation trajectories based on topological entropy, comprising the following steps: obtaining gene expression data of cells of the same cell type; standardizing the gene expression data to obtain a cell standardized expression matrix, and constructing a single-cell gene regulatory network based on the cell standardized expression matrix; constructing a single-cell gene regulatory network representation matrix based on the single-cell gene regulatory network; obtaining a single-cell differentiation trajectory and a cell topological entropy matrix based on the single-cell gene regulatory network representation matrix; and analyzing the cell topological entropy matrix and the single-cell differentiation trajectory to obtain key evolution-determining genes of adjacent states in the differentiation trajectory. The present invention identifies key evolution-determining genes based on topological entropy, and can effectively screen key nodes that affect fluctuations in the stability of the adjacent state network structure.

Provided is an embryo model, which is obtained by means of self-assembly of induced pluripotent stem cells and induced hypoblast stem cells, wherein both the induced pluripotent stem cells and the induced hypoblast stem cells are obtained by means of somatic cell reprogramming. The self-assembly of the stem cells of two different lineages generated by means of somatic cell reprogramming forms an embryo model simulating human peri-gastrulation. The embryo model better reproduces the interaction and communication mechanism among cells of multiple lineages during human embryonic development by means of combining cells of different lineages. With the embryo model, by means of a gene editing tool, lineage tracing and gene regulatory network research can be performed on cells of different lineages, thereby enabling an in-depth understanding of the molecular regulatory mechanisms of early human embryonic development.

The invention discloses an inference method and device of a gene regulation network, equipment and a storage medium, relates to the technical field of bioinformatics, solves an intercellular regulation relation matrix through a matrix decomposition framework provided by a bilateral self-characterization model, and can provide more accurate causal relation inference for intercellular regulation. The method comprises the following steps: acquiring a first gene expression matrix and a second gene expression matrix; taking the first gene expression matrix as input, performing regulation relation prediction by adopting different gene regulation inference algorithms, and constructing a global gene regulation adjacency matrix; embedding the global gene regulation adjacency matrix and the second gene expression matrix into a bilateral self-characterization model, and carrying out first solving calculation through the bilateral self-characterization model to obtain an intercellular regulation relation matrix; and on the basis of a set cell type, carrying out second solving calculation according to the second gene expression matrix and the intercellular regulation relation matrix to obtain a gene regulation network of the set cell type.

The application discloses a kind of gene sequence pre-training method and device of fusion knowledge graph, by considering the regulation relationship between genes to construct gene regulation graph, and increase motif and bin in gene regulation graph to construct knowledge graph based on gene regulation network, and then learn gene representation in knowledge graph, and the gene representation in knowledge graph is introduced as special token in the gene sequence of gene, improve the prediction accuracy of MLM model to mask, and obtain accurate gene representation, the initial vector of gene in the expansion gene regulation graph is learned as gene representation in sequence, gene representation is extracted again by pluggable representation model, such alternating process realizes the interaction of knowledge graph information and gene sequence information, gene representation is extracted using interactive training MLM model, which can improve and then improve the accuracy of gene correlation property prediction.

The invention provides a recognition method of an endometriosis biomarker based on machine learning and WGCNA (White Graphical Cell Nucleic Acid). The method comprises the following steps: constructing a lactic acid related gene diagnosis model of endometriosis; an analysis process of a patient type of the endometriosis and an immune-related function of the endometriosis is constructed; and constructing a lactic acid related gene regulation and control network of endometriosis and a lactic acid related gene targeting small molecule compound network. On the basis of endometriosis biomarkers, consensus clustering analysis and immune cell and immune function analysis are adopted, subtype classification of endometriosis is provided, and an immune target treatment strategy is provided for endometriosis patients of different subtypes.

A method for inferring gene regulatory networks from time-series single-cell RNA sequencing data includes the following steps: Step S1, data preprocessing and feature construction: For each pair of candidate genes, a three-dimensional gene joint expression histogram is constructed based on time-series single-cell RNA sequencing data as the input feature of the model; Step S2, construction of the MB-STAP model: The MB-STAP model is a supervised deep learning model, which sequentially includes an ST-MBConv3D module, a 3D-ASPP module, and a classification output layer; Step S3, model training and validation: Training samples are generated using known transcription factor-target gene regulatory pairs as prior knowledge to train and validate the MB-STAP model; Step S4, gene regulatory relationship prediction: The gene pair data to be predicted is input into the trained MB-STAP model, and the classification results representing the regulatory relationship between genes are output; This method is lightweight and can effectively identify the directionality and long-range regulatory dependence of regulation.

The invention relates to the technical field of cosmetic anti-aging efficacy evaluation, in particular to a cosmetic anti-aging efficacy evaluation method by combining a 3D skin model with gene expression analysis, which comprises a bionic skin construction module, an external data acquisition unit, a storage and analysis module and a dynamic gene regulation network analysis module. A real skin environment is simulated through a multi-layer bionic skin structure, and gene expression analysis and environmental parameter monitoring are combined, so that deep evaluation of the anti-aging effect of cosmetics is realized. Meanwhile, the data visualization module and the user interaction interface provide a visual display and operation platform, and the feedback adjustment module dynamically adjusts experiment conditions. The method can make up for the defects of a traditional method in skin structure simulation and gene analysis depth, and remarkably improves the evaluation efficiency and reliability.

The invention described herein provides compositions and reagents for assembling a tripartite complex at a specific location of a target DNA. The invention also provides methods for using the complex to, for example, label a specific genomic locus, to regulate the expression of a target gene, or to create a gene regulatory network.

The invention relates to the technical field of bioinformatics, and discloses an MICP functional gene regulatory network analysis method, device and equipment and a medium. Expression data of an MICP functional gene is binarized and mapped into a one-dimensional ordered sequence; secondly, coding the gene state sequence into a quantum state vector, and performing efficient compression expression on the quantum state vector by using a matrix product state tensor network; on this basis, quantum mutual information between the gene pairs is calculated to construct a binary regulation skeleton network, and multi-element quantum mutual information is further calculated to identify a significant multi-gene synergy or redundancy regulation module; and finally, screening a reliable relationship through statistical test, integrating and constructing a comprehensive regulation and control network, performing function analysis, breaking through the bottleneck of a traditional method on high-order relationship capture through quantization and a tensor network, and remarkably improving the robustness and analysis depth of network inference.

The present application relates to the technical field of bioinformatics, and discloses a MICP functional gene regulation network analysis method, device, equipment and medium. The expression data of the MICP functional gene is binarized and mapped into a one-dimensional ordered sequence. Secondly, the gene state sequence is encoded into a quantum state vector, and a matrix product state tensor network is used for efficient compression representation. On this basis, not only the quantum mutual information between the genes is calculated to construct a binary regulation skeleton network, but also the multi-element quantum mutual information is further calculated to identify significant multi-gene synergistic or redundant regulation modules. Finally, reliable relationships are screened through statistical tests, and a comprehensive regulation network is integrated and constructed for function analysis. Through quantumization and tensor network, the bottleneck of traditional methods in high-order relationship capture is broken through, and the robustness and analysis depth of network inference are significantly improved.

This invention discloses a method for predicting gene regulatory relationships in epithelial cells based on directed graph convolution, comprising: 1. obtaining the expression values ​​of epithelial cell genes and preprocessing the feature values; 2. obtaining epithelial cell gene sequence data and inputting the gene sequence data into a bidirectional gated recurrent unit of a recurrent neural network model to obtain the sequence features of this type of gene; 3. calculating biological features for the epithelial cell gene sequences using biologically defined formulas; 4. concatenating the features obtained in the previous steps and inputting them into a directed graph convolutional neural network to train an epithelial cell gene regulatory network and its output prediction score matrix, thereby determining whether a regulatory relationship exists between genes based on the relationship between the prediction score matrix and a threshold. This invention can accurately predict the control relationships between genes in human epithelial cells, helping researchers to study the nature of organisms more efficiently.

The application discloses a cervical cancer gene regulation network analysis method based on a multi-scale graph convolution, S1. Construct a multi-modal biological data set; S2. Construct an initial cervical cancer gene regulation network based on the multi-modal biological data set; S3. Feature extraction is performed on the initial cervical cancer gene regulation network by using an improved multi-scale graph convolution network; S4. The network structure, graph convolution kernel weight and hyperparameter of the improved multi-scale graph convolution network are globally and locally searched and optimized by using a zebra optimization algorithm, so that an optimized improved multi-scale graph convolution network model is obtained; S5. A high-confidence cervical cancer gene regulation network is generated; S6. The biological significance of key driver genes of cervical cancer in cervical cancer is verified in combination with a public database, and the analysis of the cervical cancer gene regulation network is realized. The application is helpful to improve the accuracy and biological credibility of the gene regulation network, and provides theoretical support for early diagnosis and individualized treatment of cervical cancer.

The application provides a screening method and system for markers in oocyte formation process, and relates to the technical field of gene regulation. The method comprises the following steps: determining cell-specific transcription factors and cell-specific genes based on human and mouse chromatin characteristic data and gene expression data; constructing a transcription factor-mediated gene regulation network; obtaining key oocyte-specific transcription factors by comparing human and mouse cell-specific transcription factors; obtaining key oocyte-specific genes by comparing human and mouse cell-specific genes; evaluating the expression levels of the key oocyte-specific transcription factors and the key oocyte-specific genes in embryonic stem cells, primordial germ cells and oocytes; and taking the key oocyte-specific transcription factors and the key oocyte-specific genes with expression levels higher than preset expression values as markers. The key transcription factors and genes are determined, and the efficiency of in-vitro embryonic stem cell differentiation into oocytes is improved.

The invention discloses a biomarker recognition system based on a graph convolutional neural network, and the system comprises the steps: carrying out the graph embedding of a gene regulatory network at different pathological stages, and obtaining the low-dimensional vector representation of a gene node in the gene regulatory network at different pathological stages; based on the low-dimensional vector representation of the gene nodes, clustering the gene nodes in different pathological stages by adopting a clustering algorithm; calculating a deviation score to measure the abnormal degree of the gene node in each pathological stage, and storing the gene of which the deviation score exceeds a set threshold value into an abnormal gene set; extracting a minimum dominating set in the abnormal gene set; adopting a shortest path algorithm to optimize connectivity between nodes of the minimum dominating set, and generating biomarker networks in different pathological stages; calculating dynamic network indexes based on the biomarker networks in different pathological stages; and determining a key time node of a disease development stage based on the change turning point of the dynamic network index.