51 results about "Gene regulatory network" patented technology

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.

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 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 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.

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 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 invention discloses a quick query method for a shortest propagation path with high topological connectivity, which comprises the following steps: acquiring TCN values among biomolecules in a given gene regulation and control network, and layering biomolecule pairs according to the TCN values to generate a hierarchical structure; executing a breadth-first search strategy based on the input initial biomolecule and the target biomolecule, and obtaining a shortest propagation path meeting TCN hierarchy constraints; searching hierarchies from top to bottom based on a hierarchical threshold list in the hierarchical structure to obtain a shortest path; and obtaining the shortest path with high topological connectivity. According to the method, the topological connectivity index TCN is defined, and measurement of the propagation potential between biological molecules in the gene regulation and control network is achieved. Different from existing research, the method needs to consider the path length and the topological connectivity index at the same time so as to guarantee the propagation speed and the relative stability of propagation at the same time.

This invention relates to the fields of intelligent decision-making and digital healthcare, disclosing a method, apparatus, electronic device, and storage medium for analyzing the relationship between drugs and cancer based on graph neural networks. The method includes: querying multi-omics data and gene regulatory networks of cancer information; querying molecular maps of drug information; determining cancer vectors of cancer information using a graph neural network model; determining drug vectors of drug information using a graph neural network model; constructing a cancer-drug bipartite graph of cancer information and drug information; calculating a first loss value of the graph neural network model; calculating a second loss value between cancer information and drug information; training the graph neural network model using the first and second loss values ​​to obtain a trained graph neural network model; and using the trained graph neural network model to identify the cancer-drug relationship between cancer information and drug information. This invention can improve the effectiveness of drug-cancer relationship analysis.

This invention provides a method and system for ranking drug response cell populations based on single-cell transcriptome data, relating to the field of drug response cell population ranking. The method includes: constructing a target gene regulatory network (GRN) for each cell population based on scRNA-seq data under disease states of known cell population types; virtually knocking out target points in the GRN based on drug target information to construct a target gene regulatory network (tpGRN) for each cell population after virtual target knockout; training a low-dimensional representation of each network node in the GRN and the tpGRN using manifold alignment; calculating the distance of each network node in the GRN and the tpGRN using Euclidean distance; comprehensively considering the changing trends of drug targets, 2-hop nodes, and edges of 2-hop nodes in the GRN and tpGRN to score the drug response of different cell populations, generating drug perturbation scores to rank the drug responses of cell populations and determine the ranking results. This invention can improve the accuracy of inferring drug response cell populations.

The present application relates to the technical field of biological gene expression, and particularly relates to a gene regulation network topology identification method and system based on random variation Bayesian and a storage medium. The steps are as follows: 1) obtaining a gene expression dataset to be identified; 2) reconstructing a gene regulation network model by using a dynamic structure function DSF based on a state space model; 3) estimating parameters in the gene regulation network model by using a method based on random variation Bayesian; 4) identifying the topology of the gene regulation network by using a forward selection method, updating the network model by using an ARD variable, selecting a model structure by using a lower bound function J, and drawing a gene regulation network topology graph. The present application updates the posterior distribution of a global variable by using a natural gradient based on partial data, and the calculation cost is much lower than that of a classical VI method, so the present application is suitable for identifying the topology of a gene regulation network.

The invention discloses a candidate drug screening method and system based on gene drug interaction, a medium and equipment, and belongs to the technical field of biomedicines.The method comprises the steps that an abnormal gene data set of a deafness patient is obtained, a deafness gene-candidate drug relational database is input for matching, and corresponding candidate drugs are obtained; the database construction process comprises the following steps: acquiring a deafness risk gene data set, a drug-target gene network and KEGG pathway data; mapping the deafness risk genes to a drug network to obtain a drug-deafness risk gene network; analyzing the KEGG pathway to obtain a gene regulatory network; mapping the drug target genes to the regulatory network, and retaining a drug-pathway relationship containing at least two target genes; and screening drugs consistent with the deafness risk genes and related genes in regulation direction and pathway as candidate drugs, and constructing a database. Therefore, by implementing the method, the problem that candidate drugs screened based on individual abnormal genes in the prior art are lack of pathway-level biological mechanism support and are insufficient in pertinence can be solved.

The application discloses a gene regulation network construction method and related device, and relates to the technical field of gene regulation network construction. The method comprises the following steps: downloading position data of candidate regulation elements from a SCREEN database, combining a genome annotation file and scATAC-seq data to accurately position promoters and enhancers, introducing a variational autoencoder to accurately determine enhancer-promoter pairs that exist potential interaction, associating enhancers with target genes, processing a co-expression gene network based on potential binding sites of each transcription factor of a to-be-detected object on an enhancer, enhancer-promoter pairs that exist potential interaction, and target genes corresponding to each promoter, and obtaining a gene regulation network. The application can solve the problems of failing to accurately position promoters and enhancers, being difficult to associate enhancers with target genes, being difficult to distinguish direct and indirect relationships, and having too many false positive regulation relationships.

This invention belongs to the field of bioinformatics and relates to a consensus inference method for gene regulatory networks based on adaptive feature analysis. First, by inputting the inference results of multiple algorithms and performing preprocessing, the system automatically extracts five key features: gene quantity, edge quantity, weight mean, weight variance, and network density, and calculates the consistency score among each algorithm. Second, based on the features and consistency scores, the ensemble weights of each algorithm are dynamically allocated, and a weighted Borda counting method is used to fuse them to generate a consensus ranking. The optimal screening threshold is adaptively determined based on the gene quantity and network density. Finally, the obtained consensus network is quantitatively quality-assessed, and an evaluation report including coverage, stability, and consensus strength, along with the final network, is output. This invention achieves fully automated, data-driven consensus inference, significantly reducing reliance on external parameters and prior knowledge, and providing a reliable and interpretable computational tool for accurately identifying gene regulatory relationships.

This invention relates to a collaborative UAV swarm capture method based on a predictive hierarchical gene regulatory network, belonging to the field of intelligent UAV swarm defense. First, a long-short term network is used to predict the target trajectory, and based on the predicted target and obstacle positions, a gene regulatory network is employed to obtain a pre-determined capture pattern. Second, a uniform sequence of capture points is generated on the capture pattern, and tasks are assigned to the UAVs, resulting in a specific target capture point for each UAV. Finally, after a UAV determines its assigned capture point, an improved artificial potential field method incorporating vortex obstacle avoidance force and damping mechanisms drives the UAV to move towards the assigned capture point. This invention is applicable to collaborative UAV swarm capture tasks, providing movement schemes for UAVs when they are capturing a target, ensuring efficient completion of the capture task while reducing energy consumption and avoiding collisions.

The application discloses a screening method and system of a drug target with space-time specificity and a computer device, and relates to the technical field of bioinformatics and computational biology. The screening method is based on single-cell transcriptome sequencing data, and the method comprises the following steps: (1) quantitatively reconstructing the spatial positioning and functional mode of cells in a tissue, which comprises the following steps: 1.1) performing data preprocessing on the single-cell transcriptome sequencing data; 1.2) reconstructing the spatial positioning of single cells; and 1.3) reconstructing the biological functional mode of single cells; and (2) screening a drug target with space-time specificity, which comprises the following steps: 2.1) analyzing cell-cell communication; 2.2) constructing a gene regulatory network (GRN) with a specific tissue microenvironment state as the core; and 2.3) discovering a target. The application verifies an innovative single-cell data analysis method, and provides a new thought and technical path for drug research and development of metabolic diseases and other systemic diseases, and has the basis of popularization and application.

The application is suitable for the technical field of data analysis, and provides a data analysis system and method for gene regulatory network based on deep regression algorithm, the method comprises the following steps: converting gene identity and gene expression value into vector representation; calculating attention score between gene vector representations as a judgment standard for the relationship between genes; and predicting specific gene expression value. The application enhances the anti-noise performance by introducing a Gaussian layer, captures the complex regulatory relationship between genes by using embedding and attention mechanism, and realizes individualized GRN prediction at the single cell level, which can overcome the defects of the existing method in the personalized treatment of cancer patients, and further support the gene expression analysis and prediction in the personalized treatment of cancer patients.