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16 results about "Computational gene" patented technology
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A computational gene is a molecular automaton consisting of a structural part and a functional part; and its design is such that it might work in a cellular environment. The structural part is a naturally occurring gene, which is used as a skeleton to encode the input and the transitions of the automaton (Fig. 1A). The conserved features of a structural gene (e.g., DNA polymerase binding site, start and stop codons, and splicing sites) serve as constants of the computational gene, while the coding regions, the number of exons and introns, the position of start and stop codon, and the automata theoretical variables (symbols, states, and transitions) are the design parameters of the computational gene. The constants and the design parameters are linked by several logical and biochemical constraints (e.g., encoded automata theoretic variables must not be recognized as splicing junctions). The input of the automaton are molecular markers given by single stranded DNA (ssDNA) molecules. These markers are signalling aberrant (e.g., carcinogenic) molecular phenotype and turn on the self-assembly of the functional gene. If the input is accepted, the output encodes a double stranded DNA (dsDNA) molecule, a functional gene which should be successfully integrated into the cellular transcription and translation machinery producing a wild type protein or an anti-drug (Fig. 1B). Otherwise, a rejected input will assemble into a partially dsDNA molecule which cannot be translated.
A single-cell trajectory inference method based on adaptive feature selection
This invention belongs to the field of bioinformatics and relates to a single-cell trajectory inference method based on adaptive feature selection. First, an initial gene expression matrix is obtained through data preprocessing and screening for highly variable genes. Second, a two-dimensional evaluation strategy is employed to calculate the scores of highly variable genes with gene expression variability and the trajectory importance score related to differentiation trajectories. Then, a dynamic weight fusion mechanism is introduced, adaptively adjusting the fusion weights of the two scores based on performance feedback, and highlighting key genes through nonlinear enhancement. Next, an intelligent inflection point detection algorithm adaptively determines the optimal number of features. Finally, trajectory inference is performed based on a variational autoencoder model reconstructed from feature subsets, and a performance-driven feature selection closed loop is formed through multiple rounds of iterative optimization. This invention achieves high-precision, adaptive single-cell trajectory inference, solving the technical problems of single feature selection and fixed weights in traditional methods.
Owner:LUDONG UNIVERSITY
Drug analysis and repurposing platform with synergy and disease clustering capabilities
A method for maintaining a gene description dataset associated with a plurality of genes with respect to a given disease, wherein the gene description dataset is a human- comprehensible text-based dataset generated by a Large Language Model (LLM); maintaining a dataset of drug mechanisms of action (MOAs) associated with a plurality of drugs; embedding the gene descriptions and the MOAs into high-dimensional vectors using a text embedding technique to generate gene description vectors and MOA vectors; calculating distances between the gene description vectors and the MOA vectors to generate distance scores; and ranking the drugs for the given disease based on an aggregated distance score between the MOA vectors and the vectors of genes associated with the given disease.
Owner:BIOSSIL INC
LAMA5 expression-based pan cancer prognosis evaluation system
A generic cancer prognosis evaluation system based on LAMA5 expression provided by the invention relates to the technical field of generic cancer prognosis evaluation, and comprises the following steps: acquiring multi-source molecular data, establishing a standardized data input path to obtain LAMA5 gene molecular information, and completing sample identification and field standardization by calculating mutation frequency and gene copy number variation of the LAMA5 gene molecular information to obtain a generic cancer prognosis evaluation result. And an LAMA5 generic cancer molecular database is formed. A standardized data input path and a unified field format are formed by establishing an LAMA5 generic cancer molecular database and integrating TCGA, GTEx, a human protein map and other multi-source molecular data, the database achieves generic cancer prognosis evaluation system calculation of LAMA5 mutation frequency and gene copy number variation, the data consistency and statistical reliability of multiple cancer species are improved, and the method is suitable for large-scale popularization and application. And a data basis is provided for pan-cancer level expression and variation analysis.
Owner:NINGXIA HUI AUTONOMOUS REGION PEOPLES HOSPITAL
A method and system for endometriosis subtyping
The application relates to an endometriosis subtype identification method and system, relates to the fields of bioinformatics and medical diagnosis auxiliary technologies, and takes transcriptome expression data as input, constructs a standardized expression matrix, and adopts consistency clustering to perform subtype modeling to obtain a molecular subtype label; expression timing structure of a menstrual cycle phase is introduced to perform dynamic gene identification and discriminant power evaluation, and immune cell infiltration inference and path activity scoring are combined to analyze differences of the subtypes in an immune and inflammation network, then gene-pathway / gene-cell correlation is calculated to form multi-dimensional typing information with mechanism orientation. Based on the above results, an interpretable typing identification model is constructed, which provides a new technical means for accurate diagnosis and target identification of endometriosis.
Owner:FOSHAN MATERNAL & CHILD HEALTH CARE HOSPITAL
Method for rapidly identifying plant imprinting gene based on multi-species sequence conservative property
The invention relates to the cross technical field of bioinformatics and epigenetics, and particularly discloses a method for rapidly identifying plant imprinting genes based on multi-species sequence conservation.The method comprises the steps that a whole genome gene set of target plant species is obtained; selecting reference plant species with different genetic relationships with the target plant species, obtaining an optimal homologous gene of each gene of the target plant species in the reference plant species, and constructing a multi-species homologous sequence set; performing multi-sequence comparison on the multi-species homologous sequence set, and calculating a conservative score of each target gene based on a comparison result; and sorting and screening the target genes based on the conservative scores, and preliminarily identifying the genes with the conservative scores higher than a set threshold as candidate plant imprinting genes. According to the method, high-throughput and low-cost screening and prediction of potential plant imprinting genes are realized by utilizing the characteristic that the plant imprinting genes have higher conservative property in an evolution process and calculating cross-species conservative property scores of the genes.
Owner:YANGTZE DELTA REGION INST (QUZHOU) UNIV OF ELECTRONIC SCI & TECH OF CHINA
MICP functional gene regulation network analysis method, device, equipment and medium
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.
Owner:LONGYAN UNIV
A gene expression profile classification method and system based on feature dependency and multi-objective particle swarm optimization feature selection
ActiveCN119889460BInternal combustion piston enginesBiostatisticsClassification methodsFeature dependency
The application provides a gene expression profile classification method and system based on feature dependence and multi-objective particle swarm optimization feature selection, comprising the following steps: calculating the dependence score of each feature of the gene expression profile data, quantifying the dependence relationship between features by information entropy and mutual information; initializing the particle swarm based on the dependence score, and preferentially selecting features with high dependence scores; adopting a multi-objective particle swarm optimization algorithm for feature selection, combining the classification error rate and the feature selection rate as the objective function, and realizing the balance between low error rate and feature subset selection in the optimization process; adopting an improved particle optimal position updating strategy, generating new solutions through a non-dominated solution updating mechanism based on cubic spline interpolation, and improving the global search ability of the particle swarm; and obtaining the optimal feature subset through the multi-objective optimization process. Through the optimization of the feature selection process and the improvement of the particle swarm optimization strategy, the application improves the classification performance and the efficiency of feature selection, and can effectively capture the dependence relationship between genes.
Owner:JIANGSU UNIV
A method, device and medium for analyzing a MICP functional gene regulation network
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.
Owner:LONGYAN UNIV
Differential gene identification method based on non-parametric test
The application discloses a differential gene identification method based on non-parametric test. Gene expression matrices of an experimental group and a control group are obtained. An MMD unbiased empirical estimation between gene expression level sequences of genes in the experimental group and gene expression level sequences of the genes in the control group is calculated as an original MMD unbiased empirical estimation of the genes. Gene expression levels in the gene expression level sequences of the genes in the experimental group and the control group are combined and then randomly shuffled and rearranged, and an MMD unbiased empirical estimation is calculated as a new MMD unbiased empirical estimation. The significance parameter of the genes is calculated, and the differentially expressed genes are determined according to the significance parameter. The method does not depend on specific data distribution assumptions, can more flexibly process data sets with few time points or large gene expression level fluctuations, is suitable for various types of gene expression data, and improves the flexibility and applicability of analysis.
Owner:WUHAN BOTANICAL GARDEN CHINESE ACAD OF SCI
Cardiovascular state-oriented circadian rhythm assessment method and system
The invention provides a circadian rhythm evaluation method and system oriented to cardiovascular states, and belongs to the field of transcriptional gene analysis, and the method comprises the steps: obtaining transcriptome data at a single time point; gene expression data are positioned and extracted from the transcriptome data through a preset rhythm homeostasis gene module, and the rhythm homeostasis gene module is constructed through a weighted gene co-expression network analysis algorithm; and calculating the collaboration, the volatility and the network concentration degree of the gene expression data based on a dynamic network biomarker algorithm, and determining the circadian rhythm disorder index of the to-be-evaluated user according to the collaboration, the volatility and the network concentration degree. According to the method, collaborative expression characteristics are systematically captured by using a rhythm homeostasis gene module, dependence on a small number of core clock gene signals is avoided, and quantitative calculation is carried out on the collaboration, volatility and network concentration ratio of gene expression data in the module based on a dynamic network biomarker algorithm; and an objective and quantifiable molecular evaluation index is provided for an individual rhythm state.
Owner:ZHENGZHOU UNIV
A transposon insertion variant detection and classification method based on long read sequencing data and related devices
The present application belongs to the technical field of transposon insertion variation, and relates to a transposon insertion variation detection and classification method based on long read sequencing data and a related device. The method comprises: obtaining long read sequencing data and aligning the data with a reference genome; extracting alignment signals and locating candidate insertion regions; obtaining high-confidence insertion events through multi-stage clustering and refining; extracting and standardizing insertion sequence fragments; converting the sequences into numerical features; using a deep learning model to annotate the insertion types, outputting transposon type probabilities; and finally comparing the likelihood values of different genotype hypotheses, selecting the maximum probability genotype and calculating the genotype quality score. The present application solves the problems of low calculation efficiency of traditional sequence alignment-based annotation methods and insufficient accuracy and robustness of sequence classification methods based on large models, and realizes efficient and accurate transposon insertion variation detection and genotype determination.
Owner:XI AN JIAOTONG UNIV
Cluster-based multiple differentially methylated region prediction ensemble integration method and system
The application discloses a clustering-based multiple differential methylation region predicted set integration method, which comprises the following steps: dividing a genome into a plurality of non-overlapping genomic blocks; setting a threshold and evaluating the reliability of all predicted sets under the threshold to construct a reliability weight matrix; calculating the weight of each genomic block; and clustering all genomic blocks to obtain a final integrated differential methylation region set. The application also discloses a system for implementing the clustering-based multiple differential methylation region predicted set integration method. The application calculates the weight of the genomic block based on the influence of the method coverage and the methylation difference on the differential methylation region distinguishing capability, improves the reliability of the integrated set, and can provide a more reliable, comprehensive and significant differential methylation region set by reasonably integrating the differential methylation region sets predicted by different methods. Moreover, the application is high in reliability, good in accuracy and objective and scientific.
Owner:CENT SOUTH UNIV
A genome prediction system and method based on GEP-GANGP
The application relates to the field of genome prediction, and specifically discloses a genome prediction system and method based on GEP-GANGP, which comprises the following steps: generating synthetic phenotype data by using GEP-cGAN; processing real and synthetic data by using a feature extraction network; calculating the interaction features of genotypes and environments by using a cross-modal attention mechanism; realizing knowledge transfer and feature enhancement by using a transfer learning module; synchronously outputting phenotype prediction values, effect weights and explainable features by using a multi-task decoder; and the system comprises a GEP-cGAN module, a biological multi-head attention mechanism (BMAM) module, a cross-modal interaction feature extraction module, a transfer learning (TrG2P) module and a multi-task decoder module. The application solves the problems of low prediction accuracy, poor generalization ability and weak model explainability in the small sample scene of forest tree breeding, significantly improves the accuracy and practicability of genome prediction, and provides reliable technical support for forest tree genetic improvement.
Owner:RES INST OF FOREST RESOURCE INFORMATION TECHN CHINESE ACADEMY OF FORESTRY
Single cell trajectory inference method based on adaptive feature selection
The invention belongs to the field of bioinformatics, and relates to a single cell trajectory inference method based on adaptive feature selection. Firstly, an initial gene expression matrix is obtained through data preprocessing and highly variable gene screening; secondly, respectively calculating a highly variable gene score of gene expression variability and a trajectory importance score related to a differentiation trajectory by adopting a two-dimensional evaluation strategy; then, a dynamic weight fusion mechanism is introduced, the fusion weight of the two types of scores is adjusted in a self-adaptive mode based on performance feedback, and key genes are highlighted through nonlinear enhancement; then, an intelligent inflection point detection algorithm is adopted to adaptively determine the optimal feature number; and finally, reconstructing a variational auto-encoder model based on the feature subset to carry out trajectory inference, and forming a performance-driven feature selection closed loop through multi-round iterative optimization. According to the method, high-precision and self-adaptive single cell trajectory inference is realized, and the technical problems of single feature selection and fixed weight of a traditional method are solved.
Owner:LUDONG UNIVERSITY
An anti-cancer cell sensitivity prediction method, system, device and medium fusing network relationships of genes
ActiveCN116343928Breduce noise informationreduce dimensionalityChemical property predictionBiostatisticsCancer cellPredictive methods
A kind of anti-cancer cell sensitivity prediction method, system, equipment and medium of fusion gene network relationship, method includes: the vector of the expression amount of each gene of cell line is as the original gene expression feature of cell line, compressed low-dimensional hidden vector is used as the gene expression feature of cell line by using self-encoder, graph self-encoding is carried out to gene interaction network to obtain gene interaction feature, gene network feature is calculated according to gene expression feature and gene interaction feature, the feature of each atom in drug compound molecule is obtained by establishing drug compound molecular graph, and the predicted anti-cancer cell sensitivity is obtained according to EIGA model;System, equipment and medium are used to realize a kind of anti-cancer cell sensitivity prediction method of fusion gene network relationship;The present application fully considers the interaction relationship between genes and the topological structure in compound molecule by designing new algorithm, realizes the anti-cancer cell sensitivity prediction on cell line and single cell level.
Owner:XIDIAN UNIV