202 results about "Multi omics" patented technology

The invention discloses an intestinal cancer metastasis prediction method and device based on spatial omics, a medium and equipment, and the method comprises the steps: collecting original multi-omics data, and carrying out modal alignment and quality control processing to obtain pre-processed multi-omics data comprising second spatial transcriptome data, second single-cell RNA sequencing data and second pathological image data; performing cross-modal semantic embedding on the second spatial transcriptome data based on the second single-cell RNA sequencing data to generate a spatial enhanced expression profile; performing multi-scale graph construction on the second spatial transcriptome data and the second pathological image data, and extracting spatial heterogeneity features; inputting the spatial enhancement expression spectrum and the spatial heterogeneity features into a pre-trained metastasis risk prediction model, and outputting a liver metastasis probability spatial heat map and a key driving feature list; and finally generating a clinical prediction report containing high-risk area positioning. According to the method, through dynamic optimization of spatial resolution and multi-scale feature collaborative modeling, the sensitivity of early transfer detection is remarkably improved.

The invention relates to the technical field of bioinformatics and clinical medicine, and discloses a multi-omics data association analysis-based tumor early screening and typing early warning system, which comprises a data acquisition and preprocessing module for integrating standardized longitudinal multi-omics data; the dynamics and topology analysis module is used for generating topology fingerprints representing dynamic behaviors of the system through state space reconstruction and persistent coherence analysis; the causal inference and risk assessment module is used for calculating critical moderation indexes in parallel to synthesize risk indexes and constructing a dynamic causal network; and a collaborative diagnosis and report generation module. According to the system, risk indexes derived by critical moderation, topological fingerprints and a dynamic causal network are creatively combined, multi-modal information fusion is carried out through a collaborative diagnosis unit, and finally a comprehensive early warning report is generated. According to the invention, the accuracy and reliability of early risk early warning of tumors can be obviously improved, and a mechanism-level traceability basis is provided for clinical intervention.

The invention provides a cross-omics sparse feature selection system and method based on hierarchical causal modeling, and the system comprises a data input and preprocessing module which is used for receiving multi-omics original data of a multivariate sample; the hierarchical causal structure learning module is connected with the data input and adaptive preprocessing module and is used for constructing a cross-omics hierarchical causal topology; the causal-oriented sparse feature selection module is connected with the hierarchical causal structure learning module; and the model retraining and integration module is used for constructing a three-layer weighted integration discrimination model based on the screened markers, optimizing the fusion weight of each layer through a gradient descent algorithm, and outputting a final prediction result. According to the method, the protein-metabolism biological hierarchy relationship and serum-urine complementary information are fully utilized, and the method has good generalization ability and can be widely applied to marker mining and prediction modeling of cancers, metabolic diseases and the like, so that the accuracy and reliability of precise medical treatment are improved.

The invention discloses a multi-omics cancer subtype recognition method, system and device based on density sensing cluster structure guide contrast learning and a medium, and belongs to the technical field of bioinformatics and artificial intelligence crossing. The method comprises the following steps: acquiring and preprocessing multi-omics data; constructing an omics specific auto-encoder and learning potential representation; constructing a density sensing cluster block in the potential space; constructing a cross-omics positive and negative sample pair based on cluster block sample overlapping; difficult negative sample mining; constructing a cluster block level cross-omics contrast learning target, and training and updating; a self-supervised soft refinement mechanism is introduced to dynamically enhance a cluster structure; and carrying out multi-loss joint optimization and model iteration training. According to the method, the robustness and the stability of a cancer subtype recognition result can be improved, high-dimensional, multi-source and multi-noise multi-omics data can be efficiently modeled and analyzed, good generalization ability and application potential are achieved, and reliable technical support can be provided for cancer subtype research, patient stratified analysis and precise medical aid decision making.

The invention discloses a multi-omics data integration and classification method, system and device based on hierarchical attention, and is applied to the field of precise medical big data analysis. The method comprises the following steps: firstly, generating feature embedding and feature importance scores through a plurality of parallel feature-level attention modules; then, embedding and inputting all the characteristics of the omics into a unified omics-level attention module, and generating omics embedding and omics importance scores; and finally, a classification prediction task is executed based on omics embedding, and a classification result is output for disease classification. The invention completely abandons a traditional dependency graph convolutional network and an integration normal form of variants of the dependency graph convolutional network, and provides a universal hierarchical attention integration architecture. The framework supports classification tasks of any complex diseases, is not limited by omics data types and combination modes, not only is remarkably superior to a traditional integration normal form in classification performance, but also shows a unique negative generalization distance, and proves that the framework has excellent generalization ability. Meanwhile, features and omics importance scores automatically output by the model provide a powerful analysis tool for biomarker discovery and precise diagnosis and treatment of complex diseases.

The invention provides a multi-omics data missing interpolation method and system based on a multi-view auto-encoder, and relates to the technical field of biological multi-omics data. The method comprises the following steps: firstly, acquiring multi-omics data related to a disease, and preprocessing the multi-omics data; then special encoders are constructed for different omics data; introducing a self-attention mechanism and a cross-attention mechanism to enhance the feature expression ability and realize cross-omics feature fusion based on the potential feature expressions of each group of science extracted by each encoder to obtain the fusion feature expressions of each group of science; then, adopting a hierarchical fusion strategy to further integrate feature representations of each group of chemical fusion into unified potential feature representations to realize deep fusion and compression of information; finally, specialized decoders are designed for encoders of each group for original data reconstruction; according to the method, the problem of data missing generally existing in multi-omics integration is solved.

The invention relates to the crossing field of artificial intelligence technology and bioinformatics, and discloses a pig feed efficiency prediction model and system based on multi-omics data. Modulating a neural differential equation which runs on a priori knowledge graph and is realized by a graph neural network by using the matrix so as to solve and generate a continuous evolution trajectory of an individual physiological state; and finally, aggregating the tracks, combining the constraint matrix, and outputting a feed efficiency prediction value through a second preset model. The invention further provides a corresponding prediction system which comprises a static constraint module, a dynamic core module and a prediction module. According to the method, static genetic constraint and dynamic physiological process simulation are combined, genetic differences among different individuals can be reflected, and the biological consistency and individualization precision of a prediction model are improved.

The present disclosure relates to predicting genomic instability status in biological samples using machine learning techniques with comprehensive genomic and immune profiling (CGIP) data. Particularly, aspects are directed towards performing a genomic instability test on a biological sample. Then, multi-omics data for the subject are obtained by DNA sequencing and RNA sequencing assays, including genomic alteration data for a first set of genes and expression data for a second set of immune genes. The multi-omics data are input into a machine learning model having a tree-based architecture, which is configured to analyze features by traversing paths from root nodes to terminal nodes in each tree based on values generated from the data. The model predicts a genomic instability status, which is then provided via a user interface notification or as part of a testing report.

The invention relates to the technical field of bioinformatics, in particular to a method for typing acute respiratory distress syndrome biological subtypes. The method comprises the following steps: a) acquiring multi-omics data and carrying out standardized preprocessing; the multi-omics data comprises transcriptomics data, proteomics data and metabonomics data of a biological sample source; b) constructing a similarity network of each group by using a similarity fusion network (SNF), and obtaining a uniform sample similarity matrix through multi-group network fusion and iteration; multiple collaborative principal component analysis (MCIA) is adopted to carry out dimension reduction on multi-omics data so as to realize visualization of a clustering result; carrying out multi-omics joint discrimination modeling under the guidance of SNF clustering by using a data integration analysis (DIABLO) method so as to identify key feature variables; and carrying out biological subtype classification based on the clustering result of the steps.

The invention discloses a cancer prognosis prediction method and system based on multi-omics fusion. The method comprises the following steps: acquiring multiple groups of omics data; preprocessing the plurality of groups of omics data to obtain a plurality of groups of first omics data features; inputting the plurality of groups of first omics data features into a multi-head attention-based graph convolutional network feature extraction model to obtain a plurality of groups of second omics features; inputting the plurality of groups of second group of characteristics into an attention mechanism fusion module based on biological priori knowledge guidance to obtain fusion characteristics; and inputting the fusion features into a prognostic scoring model to obtain prognostic scores. In addition, potential gene targets related to cancer prognosis are obtained by adopting an omics data feature importance evaluation method. The prognosis prediction conclusion obtained by the method is high in accuracy and high in interpretability.

The invention belongs to the technical field of gene identification, and discloses a method for integrating multi-omics data to enhance genome prediction and candidate gene identification, candidate gene identification is verified through multi-layer evidence integration, and the verification comprises priority ordering based on gene contribution scores, function enrichment analysis, generic genome network verification and CRISPR / Cas9 experimental verification. Evaluation on a corn population (n = 174) containing complete genomics, transcriptomics, translational omics and proteomics maps shows that the framework is remarkably improved in grain character prediction and is improved by 2.9-12.3% compared with a genome selection baseline, and meanwhile candidate genes verified by experiments are recognized. The invention further verifies the universality of the framework to five traits on an arabidopsis thaliana population, and provides an open source software platform to promote the practical application of the framework in a breeding plan.

Provided herein are systems, devices, and methods for processing, analyzing, and classifying biological data sets and generation of cell profiles. The data sets may include multi-omic data. Some embodiments may include the use of machine learning in training a classifier of raw multi-omic data and incorporating system biology knowledge to understand cellular behavior and cell status at the biomolecular level.

The invention provides a Wuzhishan pig organ multi-omics data space integration and analysis method, and belongs to the technical field of data statistics and analysis, the method comprises the following steps: S1, experiment design and Wuzhishan pig organ sample standardization preparation; s2, independently collecting and preprocessing multi-omics data of Wuzhishan pig organs; s3, multi-omics data standardization and batch effect correction; s4, labeling and calibrating spatial dimension information; s5, spatial specificity correlation modeling of the multi-omics data is carried out; s6, carrying out integrated clustering analysis on the spatial multi-omics data; s7, performing function annotation and path enrichment analysis on the clustering feature clusters; and S8, analyzing the spatial specific molecular mechanism and constructing a multi-omics data spatial integration database. According to the method, spatialization and systematization analysis of the Wuzhishan pig organ multi-omics data is achieved through collaborative optimization of the HHO-IK-means-BI three algorithms, and technical support is provided for experimental zoology research and conversion of medical application.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

The invention discloses a monogenic disease genetic variation intelligent interpretation method and device and a medium, and relates to the technical field of bioinformatics, and the method comprises the steps: generating an enhanced similarity matrix through a disease-specific phenotype template and a cosine similarity algorithm based on a standardized phenotype feature vector, and obtaining a dynamic weight vector through a dynamic adjustment function; integrating the dynamic weight vector into a normalized multi-omics data matrix, calculating a preliminary disturbance score through a path integral formalization algorithm, and obtaining a path disturbance score; and fusing the pathway disturbance score and the dynamic weight vector, calculating a pathogenicity score through a multi-level evidence fusion algorithm inspired by a quantum field theory, and generating a comprehensive report of variation pathogenicity grading and clinical suggestions according to a thermodynamic partition function model. According to the method, nonlinear and high-dimensional collaborative modeling and thermodynamic stability optimization of heterogeneous biological evidence are realized, and the interpretability of pathogenicity judgment under a complex genetic background is also improved.

The invention relates to the technical field of bioinformatics and molecular biology, and discloses a factor analysis system based on long-chain non-coding RNA multi-omics integration analysis. The system comprises: a multi-omics data acquisition module configured to acquire multi-modal omics data related to long-chain non-coding RNA; the data pre-processing module is configured to pre-process the multi-modal omics data to generate a data set in a unified format; the unsupervised factor analysis module is configured to integrate and analyze the data set in the unified format and identify potential factors; the heterogeneity analysis module is configured to construct a mapping relation between the potential factors and multiple omics data features; and the factor annotation and expansion analysis module is configured to perform function annotation on the analyzed potential factors based on biological function enrichment analysis, regulation and control network inference and cross-modal data association, perform missing value estimation on the multi-omics data in combination with the mapping relationship, and output an analysis result containing factor annotation information and complete data.

The invention discloses a multi-layer heterogeneous network unicellular organism network inference method based on meta-path enhancement, which mainly comprises a gene regulation knowledge base enhanced multi-layer heterogeneous network construction module for integrating an external gene interaction network and multiple omics data such as scRNA-seq, scATAC-seq, ST and the like; constructing a single-cell multi-omics multilayer heterogeneous network containing cell-cell, cell-gene and gene-gene relationships, and fusing spatial constraints to consider cell positions and tissue structures; and the feature enhancement module based on the meta-path explores complex semantics of the network by designing a multi-hop meta-path mode, designs an adaptive multi-view learning framework and a multi-round enhancement mechanism, and optimizes feature representation by using cell-gene interaction and cross-modal attention fusion. The unicellular biological network can be effectively deduced, the deduction accuracy and biological interpretation are remarkably improved, the method plays an important role in understanding the cell biological process, developing and treating diseases and the like, has good expandability, and can further integrate multi-modal omics data such as proteomics and metabonomics.

The invention discloses an acute myelogenous leukemia prognosis prediction method and device based on multi-omics fusion, and the method comprises the steps: collecting and preprocessing gene mutation data and gene expression data to construct a data set; constructing an acute myelogenous leukemia prognosis prediction model, and training by using the data set; inputting the preprocessed gene mutation data and gene expression data as genomics data and transcriptomics data into the trained prediction model to obtain a risk score of prognosis prediction; wherein the two shared encoders in the model share part of parameters, two modal features extracted by the two shared encoders are subjected to CLIP-based feature alignment, and features extracted by the private encoder and the shared encoder of each modal are subjected to feature decoupling. According to the method, complementarity and synergy of genomics and transcriptomics data are fully mined through a layered feature decoupling and dynamic fusion mechanism, and the prognosis prediction accuracy of the acute myelogenous leukemia patient is improved.

The invention discloses a hepatocellular carcinoma prognosis and immune response prediction method based on multi-omics machine learning, and relates to the technical field of biomedicine and artificial intelligence crossing, and the method comprises the following steps: S1, constructing a multi-omics data set for model training and verification; s2, performing feature integration and clustering analysis on the multi-omics data to obtain corresponding hepatocellular carcinoma molecular subtype distribution; s3, constructing a hepatocellular carcinoma prognosis model based on Cox regression combined with a random survival forest, identifying 11 core immune genes and corresponding weight coefficients by training the model, and constructing an immunotherapy response index IMLIRI score; and S4, carrying out clinical application on the IMLIRI score. According to the method, through multi-omics data integration and multi-queue external verification, the influence of data deviation and queue heterogeneity on the model performance is reduced, so that the method shows stable prediction performance in hepatocellular carcinoma queues with different sources and different pathogenesis backgrounds, and the reliability and generalizability of the model in clinical application are improved.

The invention relates to a disease-specific quantitative trait locus identification method based on multi-omics integration. The method comprises the following steps: acquiring variation sites of whole genome sequencing data of a target object, and molecular phenotypes and molecular abundance of molecular phenotype data; determining an association significance probability value of an association pair formed by the variation point and the molecular phenotype based on the variation point and the molecular abundance, and screening a first association pair from the association pair based on the association significance probability value and condition analysis; determining a consistent second association pair in the normal association pair and the disease association pair, and determining a third association pair with a disease interaction effect in the second association pair; calculating a first effect estimation value and a second effect estimation value of each third association pair; and based on the first effect estimation value and the second effect estimation value of the third correlation pair, determining a target correlation pair related to the Parkinson's disease, and taking the target correlation pair as the identified quantitative trait site. By adopting the method, the Parkinson's specific pathogenic heritable variation can be accurately identified.

The application provides a miRNA-disease relationship prediction method, system and model based on a hybrid expert model and a storage medium. The method comprises the following steps: obtaining a miRNA-disease correlation matrix of multi-omics data of a miRNA-disease to be predicted, a miRNA similarity matrix and a disease similarity matrix; constructing a miRNA-disease heterogeneous graph, a miRNA homogeneous subgraph and a disease homogeneous subgraph, and fusing them into a multi-modal biological graph network; inputting the fusion network into a pre-trained gated hybrid multi-expert network model to output a correlation probability of a miRNA-disease pair to be predicted; and the gated hybrid multi-expert network model comprises multiple expert networks, a gating network and an output layer. The application adopts a gating mechanism to adjust the weights of each expert for miRNA-disease prediction, maintains high prediction accuracy on a small-scale data set, and has the advantages of portability and light weight.

The invention relates to the technical field of forest tree hybridization, and discloses a forest tree hybridization parent precise matching method based on multi-omics analysis, which comprises the following steps: S1, obtaining multi-omics data: performing genome sequencing, transcriptome analysis, proteomics analysis and metabonomics analysis on forest tree population individuals; a plurality of omics data such as genetic variation sites, gene expression quantity, protein expression abundance and metabolite spectrums are obtained. According to the forest tree cross parent accurate matching method based on multi-omics analysis, forest tree genetic characteristics are analyzed comprehensively through multi-omics data, genomics, transcriptomics, proteomics and metabonomics data are deeply fused, genetic factors closely associated with target traits are accurately identified, and the accuracy of forest tree cross parent matching is improved. According to the method, the scientificity of parent matching in forest tree cross breeding on the molecular level is remarkably improved, the fuzziness and uncertainty of traditional judgment only according to phenotype and experience are abandoned from the source, the parent matching accuracy is greatly improved, and the breeding work is more targeted and efficient.

The invention discloses a multi-omics data integration plant gene function inference system and method based on a large language model, and the system comprises a data obtaining and processing module which is used for collecting multi-omics data and converting the data into a unified data storage format; the storage retrieval module is constructed based on an enhanced retrieval generation framework and is used for organizing and querying the data through a unified index and presenting biological information of the data in an interpretable format; the analysis module is used for establishing a hierarchical evaluation framework and a double-layer verification framework so as to ensure the reliability of the data and solve the evidence conflict problem in multi-omics data integration; and the inference and explanation module is used for establishing a large language model guide framework and completing plant gene function inference according to a preset priority sequence. According to the method, multiple omics data can be comprehensively integrated for accurate gene function inference, and the reliability and stability of gene function inference are improved under the conditions of different species and limited data availability.

The invention relates to the field of computer vision, artificial intelligence and medical image analysis, in particular to a disease prediction method for supervising multi-omics tensor fusion, and the method comprises the steps: obtaining a tensor covariance among multi-omics data according to the processed multi-omics data, and defining a target function of tensor canonical correlation analysis; according to the tensor covariance and the target function, introducing structured sparse constraint and disease supervision information, and constructing a multi-modal image correlation analysis model based on tensor; solving the multi-modal image correlation analysis model by using an alternating iteration method to obtain a typical weight of each omics data; and predicting the disease according to the typical weight of each piece of omics data. According to the supervised multi-omics data fusion method based on the tensor, high-order related information can be effectively mined, the classification accuracy of chronic diseases is improved, and powerful technical support is provided for clinical diagnosis.

The present application belongs to the field of biotechnology and genomics, and particularly relates to a method and system for determining the chromosome base number of Macrobrachium rosenbergii based on multi-omics joint analysis. The method obtains de novo assembly sequencing data and Hi-C sequencing data, generates a chromosome-level candidate assembly without presetting the number of chromosomes by using Hi-C interaction signals after primary assembly, and performs whole-genome collinearity alignment with no less than two published reference genomes; in combination with quality constraints such as collinearity continuity, Hi-C boundary characteristics and BUSCO / LAI, the candidate chromosome boundary is comprehensively judged and iteratively converged, and finally the chromosome base number and reviewable evidence chain are output. The embodiments show that the present application can identify and correct the number redundancy caused by over-splitting of the reference genome, determine the base number of Macrobrachium rosenbergii as n=57 (2n=114), and improve the objectivity and reliability of base number determination.

The invention relates to the technical field of image processing, and further relates to a tumor characteristic spectrum construction method based on CTC enrichment and multi-omics analysis. The method comprises the following steps: 1, sequentially carrying out spiral inertial focusing and deterministic lateral displacement sorting on a whole blood sample to be detected, and finishing single-cell microcavity positioning capture of circulating tumor cells in a single-cell microcavity capture array region; 2, acquiring a time-varying curve of an oxygen molecule concentration value for each positioned and captured circulating tumor cell, and acquiring a metabolic function characteristic parameter group; 3, forming a multi-omics joint feature vector in combination with the metabolic function feature parameter group; and 4, constructing a circulating tumor cell subset based on the multi-omics combined feature vector, extracting symbolic molecular features, and integrating and outputting a tumor feature spectrum. According to the invention, more stable technical support can be provided for tumor heterogeneity analysis, metastasis potential evaluation and curative effect monitoring.

The invention relates to the technical field of biological information, and particularly discloses a multi-omics genome combined genetic assessment method and device. According to the method, multi-source data such as a genome, a transcriptome, a proteome, a metabolome, a microbiome and a high-throughput phenotype are integrated by constructing a multi-omics fusion similar matrix, an adaptive dichotomy is adopted to optimize omics weights, a grid search and cross validation system is introduced, and global optimal estimation of parameters is achieved. Verification of the method on livestock and poultry breeding and plant breeding data shows that the prediction accuracy of complex characters can be remarkably improved, and particularly, the prediction precision in low heritability characters is remarkably improved. The invention further provides a corresponding device which comprises a data acquisition module, a matrix construction module, a parameter optimization module and a breeding value calculation module, comprehensive utilization of multi-omics information can be efficiently achieved, and the accuracy and calculation efficiency of breeding value estimation are improved.

Microscopy imaging that allows for multiple mRNAs, proteins and metabolites to be spatially resolved at a subcellular level provides valuable molecular information which is a crucial factor for understanding tissue heterogeneity as for example within the tumor micro environment. The current invention describes a method (High Density-SUMI-Seq) which combines the use of Spatial Unique Molecular Identifier in situ localization and identification (by in situ sequencing or sequential fluorescence hybridization) of rolonies derived from rolling circle amplification of circular oligonucleotides and in vitro sequencing of target amplified RNA or DNA in combination with SUMI identification at a subcellular level with no optical diffraction limitation in the amount of amplified target information that can be analyzed per cell. Apart from amplified RNA or DNA, the High Density-SUMI-Seq method can also be applied using linear oligonucleotides to spatially resolve proteins and metabolites to provide multiomics results.

The invention discloses a spatial multi-omics data prediction method and model based on spatial constraint and adversarial learning, and application. The prediction method comprises the steps of modal specific coding and potential variable inference; carrying out spatial dependency modeling based on a sparse variational Gaussian process; modeling potential variables of spatial dependency constraints; carrying out multi-modal potential representation alignment based on adversarial learning; modal specific decoding and cross-modal feature prediction are carried out; and combining objective function construction and model training. According to the embodiment of the invention, space coordinate information is fully utilized to restrain potential representation, an adversarial learning mechanism is introduced to realize effective alignment of potential features of different modals, cross-modal feature prediction is realized on the basis, and the precision, stability and application value of space multi-omics data analysis are improved.

The application discloses a pig 80K functional site gene chip and application thereof, and first designs a gene chip by using pig genome promoter, enhancer, coding region key mutation and the like information, classifies functional sites according to importance based on large-scale multi-omics high-throughput sequencing data, develops a functional site gene chip mainly taking functional sites as the main part, breaks the linkage dependence theory, and solves the problem that the existing commercial chip has poor commonality among different populations due to different linkage degrees among different populations. The chip also has the characteristics of high capture property, low cost and flexibility, and is suitable for commercial application such as pig breed genetic breeding research and selection and matching.