128 results about "Gene regulatory network" patented technology

The invention relates to a method for regulating and controlling gene expression of lactic acid bacteria in shrimp acid fermentation based on systematic biology. The method comprises the following steps: constructing a gene regulation network of lactic acid bacteria by using whole genome data and a high-throughput sequencing technology comprising RNAseq; through clustering analysis and network reconstruction, transcription factors and enzymes of key metabolic pathways of lactic acid generation, acid resistance and glycometabolism are identified, and a dynamic regulation and control model of gene expression is established; a GeneNet or Cytoscape software tool is used, genomics, transcriptomics and metabonomics data of lactic acid bacteria are fused in a multi-layer network, a metabolism regulation and control system is constructed, and the metabolism regulation and control system comprises gene expression regulation and control and real-time change of metabolic flow; the method comprises the following steps: constructing a metabolic flux dynamic model of lactic acid bacteria based on a metabolic flux analysis MFA and a dynamic system modeling technology, and analyzing a mutual relation between a lactic acid generation path and a byproduct generation path; fBA is analyzed through flow equilibrium, and the metabolic network of lactic acid bacteria is optimized.

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 invention discloses lnc.3024.2-GbMYB2 for regulating and controlling synthesis of gingko flavonoids and application of the lnc.3024.2-GbMYB2, and belongs to the technical field of genetic engineering, aiming at analyzing the biological functions of lncRNA (lnc.3024.2) and MYB target gene (Gb06719) participating in gingko flavonoids anabolism, by utilizing the technical means of bioinformatics analysis, real-time fluorescence quantification, subcellular localization, prokaryotic expression, genetic transformation and the like, the GbMYB2 can be used for regulating and controlling synthesis of the gingko flavonoids. The regulation mechanism of the transcription factor lnc.3024.2 and the target gene GbMYB2 of the lnc.3024.2 participating in the synthesis of the flavonoids in the ginkgo leaves is discussed. And a gene regulation network model of gingko flavonoid anabolism is further enriched. On the basis, the molecular mechanism of regulating and controlling the synthesis of the gingko flavonoids by the lncRNA targeting MYB is further analyzed on the early-stage basis, the content of the gingko flavonoids is increased by utilizing a genetic engineering technical means, and a new way is provided for researching the synthesis way of the gingko flavonoids.

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 belongs to the related technical field of gene regulation and control networks, and provides a gene regulation and control network construction method, system, equipment and medium in order to overcome the defects of the existing gene regulation and control network in the aspects of interpretability, authenticity and the like. Based on a graph neural network, efficient gene embedding representation learning is realized to capture a global dependency relationship; and then based on different types of regulation and control relationships, analyzing a linear causal relationship and a nonlinear causal relationship, integrating the two channels to obtain a confidence coefficient matrix of the gene, and further constructing a gene regulation and control network. The gene regulatory network constructed by the invention can reveal the regulatory relationship between genes, the interpretability, authenticity and credibility of the gene regulatory network are improved, and a more accurate and reliable tool is provided for analyzing a complex biological system subsequently.

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 embodiment of the invention provides a gene regulation network inference method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring a first time sequence corresponding to a target cell type; the first time sequence comprises accessible chromatin sequencing data and single cell transcriptome sequencing data at different first time points; constructing a corresponding first gene regulation network according to the accessible chromatin sequencing data at each first time point; pruning the first gene regulatory network based on single cell transcriptome sequencing data to obtain a second gene regulatory network corresponding to each first time point; and deducing a plurality of second gene regulatory networks corresponding to the first time sequence based on a pre-constructed gene regulatory network prediction model to obtain a target gene regulatory network corresponding to the target cell type at a second time point, the second time point at least comprising a future time point and / or a missing time point in the first time sequence. The method can improve the inference accuracy of the gene regulatory network.

Method and systems for sorting drug-responsive cell population based on single-cell RNA sequencing (scRNA-seq) data are disclosed. In some examples, the method includes: constructing a target gene regulatory network (GRN) for each of the cell populations based on scRNA-seq data of cell populations in a disease state; virtually knocking out targets in the GRN, and constructing a target-perturbed gene regulatory network (tpGRN); acquiring a low-dimensional representation of each of network nodes from the GRN in the GRN and the tpGRN through manifold alignment; calculating a Euclidean distance for each of the network nodes between the GRN and the tpGRN through a Euclidean distance; scoring a drug response of each of the cell populations by comprehensively considering changing trends of a drug target, a 2-hop node, and an edge of the 2-hop node in the GRN and the tpGRN, and determining a sorting result of the drug-responsive cell population.

The invention discloses a prediction model generation method and device of a gene regulatory network, a server and a medium, and relates to the technical field of computers. Gene activity changes at different time points in a gene expression time sequence are captured through a gene expression matrix encoder to obtain global gene expression characteristics; a text encoder is used for analyzing the corresponding relation between a gene expression mode and text semantics, multi-time-point gene expression feature data and biological text feature data are aligned through a comparative learning method, the learning rate is gradually and smoothly reduced through a cosine recession learning rate scheduling mechanism, it is ensured that the model is closer to a globally optimal solution, and the optimal solution is obtained. Relevance modeling between multi-time-point gene expression data and biological text description is achieved through the prediction model of the gene regulation and control network, and the precision and generalization ability of a statistical model are improved.

The invention relates to the technical field of biology, in particular to an engineering strain for dynamically regulating glucose metabolism to redirect 3-FL production and a construction method of the engineering strain. According to the invention, a gene-coded metabolic switch is adopted and is focused on dynamically regulating and controlling the level of phosphofructokinase (Pfk-I), so that the metabolic state is purposefully converted from a cell growth mode based on a wild strain metabolic pathway to a 3-FL production mode based on an escherichia coli anabolism pathway, and rewriting of a gene regulation network is realized; and the condition of converting the metabolic state from a cell growth mode to a production mode in the batch fermentation of the 3-FL is optimized, so that the production titer and yield of the 3-FL directly fermented from the glucose are improved.

The invention discloses an ensemble learning and deep learning-based enhancer prediction method, which comprises the following steps of: firstly, performing enhancer prediction on multi-dimensional epigenetic signal data by utilizing a Blending-KAN model so as to identify an enhancer region, and on the basis, aiming at the region which is predicted as the enhancer, segmenting the region into subsequences through a sliding window so as to obtain an enhanced region; the method comprises the following steps: firstly, constructing a plurality of sub-sequences, further predicting the probability that each sub-sequence is an enhancer through a Stacking-Auto model, and finally, accurately positioning a complete enhanced sub-region by adopting a dynamic threshold algorithm based on the probability values; according to the multi-model combination method provided by the invention, the accuracy and fine positioning capability of enhancer identification are effectively improved, and the method is expected to be applied to more complex gene regulation network researches.

Systems for inferring spatially-varying regulatory networks, such as gene regulatory networks, are provided. Optimization processes are performed and include regularization minimization processes that rely on one or both of a smoothly-changing minimization process and a sparsely-changing minimization process, for example to learn sparsely- or smoothly-changing Gaussian Markov random fields (GMRF). The systems are further able to convert spatially-varying data, such as spatial transcriptomic data, into spatio-temporal regulatory networks.

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 gene regulatory network construction method and a related device, and relates to the technical field of gene regulatory network construction.The method comprises the steps that position data of candidate regulatory elements are downloaded from an SCREEN database, a genome annotation file and scATAC-seq data are combined, a promoter and an enhancer are accurately positioned, a variational auto-encoder is introduced, and a gene regulatory element is constructed. The method comprises the following steps: accurately determining an enhancer-promoter pair with potential interaction, associating an enhancer with a target gene, and processing a co-expression gene network based on a potential binding site of each transcription factor of a to-be-detected object on the enhancer, the enhancer-promoter pair with potential interaction and the target gene corresponding to each promoter. And obtaining the gene regulatory network. The method can solve the problems that the promoter and the enhancer cannot be accurately positioned, the enhancer and the target gene are difficult to associate, direct and indirect relationships are difficult to distinguish, false positive regulation relationships are excessive and the like.

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 present application discloses a method, device, server, and medium for generating a prediction model of a gene regulatory network, which relates to the field of computer technology. The gene expression matrix encoder is used to capture the gene activity changes at different time points in the gene expression time series to obtain global gene expression features. The text encoder is used to analyze the correspondence between gene expression patterns and text semantics. Through the method of contrastive learning, the gene expression feature data at multiple time points is aligned with the biological text feature data. The cosine decay learning rate scheduling mechanism is used to gradually and smoothly reduce the learning rate to ensure that the model is closer to the global optimal solution. The prediction model of the gene regulatory network is used to establish the correlation modeling between the gene expression data at multiple time points and the biological text description, improving the accuracy and generalization ability of the statistical model.

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.

A computer-implemented method of constructing a gene regulatory network for a set of cells. A gene regulatory network is a network showing interactions between a plurality of genes. Data including a set of gene quantity values is received, each gene quantity value being associated with a combination of a cell of the set of cells and a gene of the cell. Each gene quantity value is indicative of a measurement of a gene quantity for the gene, a gene quantity being a quantity of a product resulting from the expression of the gene. A plurality of sets of the gene quantity values are determined, and for each set, a time value is determined from the gene quantity values. For each of a plurality of genes, the level of the gene quantity value for the gene over time is determined from the determined times of the sets. A wave transition time for the gene quantity value for each gene is then determined from the determined the level of the gene quantity value over time. A wave transition time is indicative of the time at which a gene quantity value passes from one steady state to another steady state. A set of interactions between the genes is then determined using the wave transition times, and a gene regulatory network is constructed from the determined set of interactions.

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 present invention discloses a method for constructing a gene regulatory network based on a generative flow network, comprising the following steps: conducting experiments on the sc-RNA-seq dataset of mouse hematopoietic stem cells to evaluate the performance of constructing a gene regulatory network. Using a sparse Transformer to perform sparse feature extraction on the scRNA-seq data to facilitate more accurate subsequent construction of a gene regulatory network; after the sparse feature extraction, inputting the sparse features into a generative flow network model for constructing a gene regulatory network. Through multiple iterative updates of the generative flow network model, a probability distribution of gene regulatory relationships associated with the scRNA-seq dataset of mouse hematopoietic stem cells is obtained, and the final gene regulatory network is extracted by setting a regulatory extraction threshold. The present invention effectively alleviates the problem that existing methods are difficult to capture complex gene regulatory relationships from sparse scRNA-seq data, and can be used as an effective tool to provide reference for medical researchers to analyze gene regulatory mechanisms.

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.