17 results about "Gene association" patented technology

This invention provides compositions and treatment methods for reducing hyperphenylalaninemia. [Solution] A genetically modified bacterium is provided, comprising: a) one or more genes encoding phenylalanine ammonia lyase (PAL), which are operably linked to a promoter that is not naturally associated with the PAL gene and can be directly or indirectly induced; b) one or more genes encoding a phenylalanine transporter, which are not naturally associated with the phenylalanine transporter gene and can be operably linked to a promoter that is directly or indirectly induceable; and c) one or more genes encoding mutant fumarate and nitrate reductase (FNR), which are not naturally associated with the FNR gene and can be operably linked to a promoter that is directly or indirectly induceable.

The application discloses a gene data processing method and device, computer equipment and a storage medium, and belongs to the technical field of computers. Through a gene function query request of a to-be-tested gene, the application can call a gene association model corresponding to a cell type to which the to-be-tested gene belongs, mine a nonlinear association degree between the to-be-tested gene and a known candidate gene, and use the candidate gene with a higher nonlinear association degree to label function annotation information of the to-be-tested gene. The way of calling the gene association model to extract the nonlinear relationship is completely different from the way of extracting the linear relationship in traditional statistics, can deeply mine the candidate gene with a higher similarity to the to-be-tested gene, and the similarity is not a linear similarity but an implicit nonlinear similarity, so that the accuracy of the gene data processing process is greatly improved.

The application provides a multi-party medical privacy data security sharing method based on privacy protection and TEE. The method comprises the following steps: obtaining local calculation results of multiple participants; the distribution frequency of alleles comprises a first frequency of a first gene appearing at a first site on a SNP, a second frequency of a second gene appearing at a second site, a third frequency of a first genome appearing, and a fourth frequency of a second genome appearing; the local calculation results of each participant are combined to obtain a to-be-analyzed coordinate site of each participant; the to-be-analyzed coordinate site is clustered to obtain a first center point of a first coordinate site and a second center point of a second coordinate site, and the real frequencies of the first gene, the second gene, and the first gene and the second gene appearing at the same time are determined; the real frequencies are used for correlation degree calculation to determine the gene correlation between the genes at the first site and the second site on the SNP. The application determines the gene correlation between the SNP sites, which is convenient for the research on genes.

The invention belongs to the technical field of genes, discloses a method for detecting and mining associated genes and regulatory pathways by using double-layer optimization, solves the black box problem in a machine learning prediction process and provides biological mechanism explanation. The previous research mostly utilizes multi-omics data to predict phenotypes, but a machine learning mode is difficult to really evaluate the action size and action approach of genes, and lacks interpretability at the same time. According to the RADAR, a parameter is given to each omics of each gene for double-layer optimization solution, phenotypes can be explained to the maximum extent, and meanwhile the action degree of each omics can be evaluated. In addition, RADAR can process input data of any number of omics, and a program can automatically identify the number of the omics and calculate the effect size of each omics. RADAR can accept omics input of different gene numbers, and the actual data condition is better met.

The invention discloses a scoring and sorting method and system for pathogenic mutation of a single-gene genetic disease, and relates to the technical field of biomedical treatment, the method comprises the following steps: based on pre-acquired phenotypic data and literature abstracts, performing standardized phenotypic term extraction on pre-acquired symptom description by using a mixed strategy, performing phenotype and gene association degree scoring on the standardized phenotype terms and pre-acquired gene mutation data through a similarity comparison method; performing gene variation annotation on a pre-acquired gene variation database, and performing gene variation scoring on the pre-acquired gene variation data according to a gene variation annotation result; and performing multi-dimensional pathogenicity comprehensive scoring evaluation by using a supervised learning model and a weighted summation method to obtain a pathogenic mutation sorting scoring result. By recommending the most probable pathogenic mutation, the grading and sorting of the pathogenic mutation of the genetic disease have the advantages of high detection recall rate and high automation degree.

The invention discloses a multi-gene interaction network prediction method and system based on a graph neural network. The method comprises the following steps: S1, acquiring multi-source biological data; s2, carrying out pretreatment; s3, constructing a gene map; s4, fusing the edge weight matrix and the node feature matrix in the gene map through a dynamic attention mechanism, and outputting a refined feature set; s5, acquiring an optimal hyper-parameter set by using a Bayesian optimization algorithm; s6, based on the gene map and the optimal hyper-parameter set, training the attention enhancement map neural network to obtain a multi-gene interaction network prediction model; and S7, predicting the interaction relationship of the unknown gene pair by using the multi-gene interaction network prediction model, and outputting a result. According to the method, the gene map is constructed through multi-source data fusion, the gene association information is enriched, the multi-head attention map neural network is introduced to automatically learn the interaction weight, the hyper-parameters are dynamically adjusted in combination with Bayesian optimization, and the prediction accuracy and model universality of the multi-gene interaction network are improved.

The invention relates to the field of bioinformatics, in particular to a susceptibility gene visualization decision support system and a method thereof.According to the system, a multi-level heterogeneous network of family and gene variation is constructed, topological feature extraction and Laplacian spectrum analysis are combined, a high-risk variation transmission path is recognized, and a high-risk variable transmission path is obtained; multi-scale persistent homology analysis is utilized to reveal risk structure hierarchies, dynamic display of a family-gene association map is realized through interactive visualization, finally, a risk hierarchy tree and a clinical knowledge base are integrated, accurate clinical decision support is provided for users, a complete analysis process from data to decision is realized, and the risk analysis efficiency is improved. A multi-level heterogeneous network is constructed based on a topology invariant theory, and integration expression of family relationship and gene variation data in a unified topology space is realized for the first time, so that doctors can intuitively understand distribution and association modes of variation in families.

This invention provides a method and application for identifying molecular markers associated with tail fat weight in sheep based on a multi-omics strategy. The method is based on a comprehensive analysis of whole-genome selection signal analysis, whole-genome association analysis of tail fat weight, and epigenomic and transcriptomic sequencing results of tail adipose tissue in sheep with different tail types. The marker is located at position 51760995 on chromosome 13 of the sheep reference genome Oar_rambouillet_v1.0, where an A / C base mutation exists, which is significantly correlated with tail fat weight. Amplification of the genomic region containing the SNP site statistically verified that the tail fat weight and relative tail fat weight of individuals with the AA genotype are significantly lower than those with the CC genotype. By detecting the genotype of the molecular marker, this invention allows for the selection of homozygous AA sheep for breeding in the core population, thereby reducing tail fat deposition and improving the economic benefits of sheep farming.

This invention belongs to the interdisciplinary field of bioinformatics and traditional Chinese medicine (TCM), and discloses an algorithm for the improvement and prediction of TCM compound prescriptions, solving the problems of strong subjectivity, long cycle, and low efficiency in traditional TCM compound prescription improvement. The algorithm first acquires and preprocesses omics sequencing data from disease and healthy groups, then establishes TCM-target gene associations based on the HIT2 and TCMSP databases, and constructs a three-dimensional association matrix of TCM-target genes-5hmC loci for epigenomic data. After dimensionality reduction and extraction of core principal components using PCA, the sample-TCM association score is calculated and a matrix is ​​constructed. The p-value is obtained through t-test or Wilcoxon rank-sum test, and converted into DAS values ​​to quantify the regulatory activity of TCM, thus screening TCM for compound prescription improvement. This invention's algorithm is objective, highly targeted, efficient, and widely applicable, adaptable to various omics data and disease scenarios. Its effectiveness has been verified in a coronary atherosclerosis case, providing precise technical support for TCM compound prescription screening and optimization, personalized TCM treatment, and the development of innovative TCM drugs.

The application relates to the technical field of canine genotype detection, and discloses a canine genetic disease genotype detection algorithm system and electronic equipment, which comprises a gene sequence feature extraction module, a site polymorphism identification module, a variation site screening module, a genotype grouping operation module, a pathogenic gene correlation module and a detection result output module. Each module is sequentially connected to realize efficient data transmission and processing. The variation site screening module, the genotype grouping operation module and the pathogenic gene correlation module each comprise four functional units, which are used for completing site screening, genotype grouping and pathogenic gene correlation analysis through multi-step fine processing. The system is provided with a built-in special analysis model, can integrate multidimensional data features to carry out detection operation, realizes whole-process processing from raw data to a standardized detection report, significantly improves the accuracy and comprehensiveness of canine genetic disease genotype detection, and provides reliable technical support for canine genetic disease diagnosis, breeding optimization and prevention and control.

The invention discloses a peanut drought-resistant gene associated molecular marker screening optimization system and method, and relates to the technical field of agricultural biology. The system comprises a data acquisition module, a molecular marker analysis module and a data integration and evaluation subsystem, peanut DNA, physiological indexes and environmental data can be acquired, candidate markers are identified through PCR amplification and high-throughput sequencing, and a comprehensive evaluation model is constructed to calculate correlation coefficients. The method comprises the steps of sample preparation and pretreatment, molecular marker preliminary screening, marker verification and optimization and application verification, setting of a drought and normal moisture control environment, elimination of false positive markers through repeated verification, screening of high-correlation markers with a correlation coefficient greater than or equal to 0.6, and application accuracy greater than or equal to 85%. The method solves the problems of low accuracy, poor environmental suitability and the like in the prior art, improves the marker screening accuracy and stability, can efficiently assist in cultivation of new drought-resistant peanut varieties and relieve the restriction of drought on the peanut industry, and has important scientific research and application values.

The invention discloses a rice grain folic acid content related SNP molecular marker and application thereof. According to the invention, the content of folic acid in rice germplasm resource population grains is determined, and candidate gene association analysis is combined to identify that two SNP loci significantly associated with the content of 5-methyltetrahydrofolic acid in rice grains exist in the rice folic acid synthesis bifunctional enzyme hydroxymethyl dihydropterin pyrophosphatase / dihydropterioic acid synthase gene; sNP1 is located in a gene promoter region-1675bp, the polymorphism is G / C / T, SNP2 is located in a gene promoter region-1624bp, the polymorphism is C / T, the folic acid content of rice grains carrying SNP1 (G / C) and SNP2 (C) is remarkably higher than that of SNP1 (T) and SNP2 (T) type rice materials, the SNP1 (G / C) and SNP2 (C) are excellent genotypes for folic acid accumulation, and the two SNP molecular markers can be used for breeding folic acid-rich rice varieties.

The application discloses a method for screening complex disease drugs based on whole genome association signals, and comprises the following steps: screening and processing complex disease cells through a DESE algorithm based on whole genome association signals, so as to obtain a cell line of the complex disease; inducing and analyzing the cell line of the complex disease through drug blank control, so as to obtain a specific perturbation spectrum of the drug on the gene; and performing cyclic prediction analysis and calculation on the specific perturbation spectrum of the drug on the gene in combination with conditional data, so as to obtain a complex disease susceptible gene drug. Through the application, the screening range of complex disease candidate drugs can be reduced by predicting the drug for specifically perturbing multiple disease susceptible genes. The application can be widely applied to the technical field of high-throughput drug screening.

The invention provides an intelligent disease identification and classification method and system based on multi-modal data fusion, and the method comprises the steps: S1, collecting multi-modal data of a medical image, a physiological signal and genome data, and carrying out the preprocessing of the multi-modal data; s2, extracting multi-scale features from the medical image through a convolutional neural network, extracting time sequence features from the physiological signal by using a bidirectional LSTM network, and extracting gene association features from the genome data based on a graph neural network; s3, fusing the three modal features by using a self-attention mechanism, dynamically adjusting the weight, and generating a global feature; and S4, inputting the global features into a classification network, and completing disease classification based on a cross entropy loss function. The system comprises a data preprocessing module, a multi-modal feature extraction module, a fusion module and a classification module, multi-modal deep fusion, self-attention weight distribution and modular design are provided, and the accuracy, robustness and clinical adaptability of disease classification can be improved.

The invention discloses a cytodynamic modeling and gene regulation network construction method and system based on RNA rate, and the method comprises the steps: obtaining single cell RNA sequencing data, and constructing a gene expression matrix according to the unspliced abundance and spliced abundance of a gene; constructing a transcription kinetic model based on an ordinary differential equation, and modeling transcription kinetic parameters into functions about an unspliced abundance vector, a spliced abundance vector, a cell embedding vector and a prior gene regulation and control network; building a deep learning model architecture, constructing a loss function through a cellular level and a gene level, and guiding the deep learning model to fit the function to obtain transcription kinetic parameters; and extracting an attention matrix from the deep learning model obtained by training as an inferred single-cell gene regulation and control network. According to the method, the problems of inaccuracy in inference and sensitivity to data noise caused by neglecting gene association in an existing method are effectively solved.

This invention relates to a disease similarity prediction method and system based on multivariate data fusion. The method includes: collecting gene network data, miRNA network data, disease-gene association matrices, and disease-miRNA association matrices to form a unified heterogeneous biological network; extracting a multimodal feature set through graph convolutional networks and improved graph attention networks; calculating the independent weights of each feature in the multimodal feature set using a multilayer perceptron, and combining the association matrices to generate disease-specific gene-perspective embeddings and miRNA-perspective embeddings, which are used as model inputs to train a lightweight bilinear tower model; optimizing the model parameters through an alternating training strategy and a ReconBoost-inspired regularization mechanism to obtain an optimized lightweight bilinear tower model, and obtaining the similarity prediction results for target disease pairs. This effectively overcomes the shortcomings of existing technologies in multivariate data integration, sparse data processing, and prediction stability.

This application provides a key gene network search method and system based on intelligent agents, belonging to the field of gene search technology. The method includes: acquiring multiple search data sets for searching key genes of target genes; for each search data set, scoring each gene in the whole genome based on the search data; determining a candidate gene set corresponding to the search data set from the whole genome based on the scoring results; performing set operations on the candidate gene sets corresponding to each search data set to obtain a target candidate gene set; for each target candidate gene in the target candidate gene set, treating the target candidate gene as a network node, and determining the node attributes of the network node based on the score of the target candidate gene in each candidate gene set; generating edges between each network node based on preset gene association evidence and each target candidate gene to obtain a key gene network. This application can improve the accuracy of key gene search.