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6 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
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
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
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
Method and device for identifying a group of orthologous genes in a pan-genus family
The application discloses a method and device for identifying orthologous gene groups in a pan-genetic family, and belongs to the field of bioinformatics. The method first acquires a phylogenetic tree, a sequence set and a target species number of genes to be grouped, and calculates a similarity matrix of gene pairs; then, a data-driven 5th percentile (P5) is used to automatically infer a split threshold, and an initial gene grouping is performed in combination with the topological structure of the phylogenetic tree; subsequently, a post-processing pipeline including isolated leaf node redistribution, micro-group topological absorption, adjacent group merging and super-large gene group targeted re-splitting is executed; finally, based on a multi-dimensional quality score function and Latin hypercube sampling, parameter optimization iteration is performed, and the best orthologous gene group (OGG) is output. The application breaks the limitation of traditional clustering which only depends on sequence similarity, effectively eliminates over-merging, fragmentation and multi-lineage genome errors, significantly improves the accuracy and evolutionary rationality of gene grouping of complex polyploid species, and has important significance for dividing core and non-core genes in pan-genome analysis. The specific flowchart is shown in FIG. 1.
Owner:BEIJING SHENGXINBANG BIOTECHNOLOGY CO LTD