301 results about "Multi omics" patented technology

A federated distributed computational system enables secure, multi-institutional biological data analysis and genomic medicine through interconnected, decentralized nodes in a federated distributed graph architecture. A federation manager coordinates computational resource allocation, control and data flows, establishes privacy and security boundaries, implements multi-scale spatiotemporal analysis and simulation modeling, models cross-species or intrapopulation elements, and maintains cross-institutional knowledge relationships. Each node includes a local processing unit for biological data analysis, including multiomics and gene editing, privacy-preserving protocols for secure multi-party computation, a hierarchical knowledge graph for managing multi-domain biological relationships across spatial and temporal scales, and encrypted network connections. The system implements cross-species genetic analysis via phylogenetic integration, environmental response modeling through spatiotemporal tracking, and multi-scale tensor-based data integration with adaptive dimensionality control. This architecture enables research institutions to collaborate on complex biological analyses and genomic medicine applications while maintaining strict data privacy and security controls.

A federated distributed computational system enables secure collaboration across institutions for unified biological and multiomics data analysis. It comprises interconnected computational nodes managed by a central federation manager. Each node includes specialized components: a local computational engine for biological data processing, a privacy-preservation system, a knowledge integration component leveraging dynamic knowledge graphs, and a secure communication interface. The federation manager coordinates computational activities while ensuring security, privacy, legality, and contractual adherence. This architecture allows institutions, citizen scientists, and patients to collaborate on complex biological analyses without compromising sensitive data. By enabling shared computational resources and expertise, the system facilitates breakthrough discoveries while maintaining confidentiality. Additionally, it supports pro-rata or contractually defined participation in resultant benefits or knowledge, ensuring equitable collaboration.

The embodiment of the invention discloses a life omics research method and device based on artificial intelligence, and the method comprises the steps: carrying out the preprocessing of a project according to the intelligent interaction between a user and a system, and generating standardized data; and meanwhile, carrying out transfer learning on the large language model to construct a life science large language model. And according to the standardized data, decomposing a research target by adopting a life science big language model in cooperation with an intelligent agent, and generating an analysis plan. And based on the analysis plan, the intelligent agent is scheduled through the coordinator, and a hierarchical task is generated. And calling a multi-omics analysis tool by the intelligent agent to calculate and analyze the grading task, and outputting an analysis result. And based on data features, integrating analysis results through an integrated advanced model, and outputting a final report to complete the research of life omics. According to the embodiment of the invention, full-process automation and natural language interaction are realized, the standardization of data analysis and the repeatability of results are ensured, the research efficiency is remarkably improved, and the technical threshold is reduced.

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.

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 multi-omics causal structure relation learning method based on comparative learning, which comprises the following steps: firstly, respectively constructing corresponding encoders for preprocessed gene mutation and gene expression data, and respectively carrying out feature extraction on two kinds of omics data; then, constructing a projection head with shared parameters to realize cross-modal feature alignment; then, using the aligned features as nodes, and constructing causal graph data through a learnable causal graph structure; constructing a graph neural network to learn causal graph representation, and constructing a contrast loss function; and finally, a model prediction result is obtained through a multi-layer perceptron, a survival prediction loss function is constructed, and a total loss function is obtained for multi-omics causal structure model training. Based on gene mutation and gene expression data, a cross-omics causal structure relationship is constructed and learned through comparative learning, more accurate prognosis prediction is provided for diseases such as acute myelogenous leukemia and the like, and potential biomarkers and key regulatory factors are helped to be found.

The invention relates to the technical field of biomedicine, in particular to a hepatic cell differentiation degree evaluation method based on multi-omics data, which comprises the steps of sample collection and preprocessing, transcriptomics, proteomics and metabonomics data analysis, multi-omics data integration and modeling and result output. By integrating multi-level biological information, a multi-dimensional scoring model is constructed, the liver cell differentiation state is quantitatively evaluated, and a standardized grading system is provided for clinic. The method can solve the limitation of single omics analysis, improves the evaluation accuracy and reliability, has universality, can be popularized to other malignant tumor research, and assists precise medical development.

Methods for assessing disease, such as multi-omics methods, are described herein. A multi-omics method can integrate proteomic data, transcriptomic data, genomic data, lipidomic data, or metabonomic data. The method screens for a disease or a disease state. Also described herein are methods of screening a disease or a disease state from a biological sample. Multi-omics databases and methods of using them are also described herein.

The invention provides an IBS micro-ecological transplantation intelligent prediction method and system based on multi-omics driving, and relates to the technical field of biomedicine. The method comprises the following steps: establishing a multi-omics data fusion subsystem to collect metagenome, metabolome, host genome and clinical phenotype group data of a target patient; inputting the data into a flora-metabolite combined network analysis model to construct an interaction network and extracting features; generating an incidence matrix based on the features and the host genome data and calculating indexes; generating indexes through a dynamic response algorithm in combination with the clinical phenotypic data and the indexes; and outputting a curative effect prediction result by using a transfer learning framework combined with modeling. The system comprises a data acquisition module, a network analysis module, a correlation calculation module, a dynamic response module and a joint modeling module. According to the method, multiple omics data are integrated, the flora and host relation is accurately mined, intelligent prediction of the micro-ecological transplantation curative effect is achieved, powerful support is provided for IBS personalized treatment, and meanwhile data processing and safety guarantee measures are taken.

The invention relates to the technical field of teasel breeding, and discloses a teasel germplasm resource multi-character collaborative screening breeding method, which comprises the following steps: multi-omics data acquisition: extracting teasel germplasm leaf DNA (deoxyribonucleic acid), and capturing exon regions of disease resistance and metabolism related genes by adopting a targeted sequencing technology to obtain an SNP (single nucleotide polymorphism) marker matrix; the method comprises the following steps: collecting root extracts, detecting medicinal components through ultra-high performance liquid chromatography-mass spectrometry, and constructing a metabolite abundance matrix; measuring phenotype data of plant height, root length, root weight, root rot morbidity and survival rate under drought stress; key site and metabolite correlation analysis: constructing a Bayesian network containing SNP markers, metabolite and phenotypes by using a graph database, and carrying out cross validation through a leave-one-out method. The dipsacus asper germplasm resource multi-character collaborative screening breeding method aims at solving the problem that a traditional breeding method lacks systematic integration analysis of genomes and metabolic pathways in dipsacus asper multi-character collaborative improvement.

The invention discloses a spatial multi-omics data integration method based on graph attention and a multivariate loss function. Comprising the following steps that spatial multi-omics data and spatial position coordinates corresponding to the spatial multi-omics data are obtained, a preset spatial multi-omics data model is input, and the model comprises a graph attention encoder and a decoder; the spatial multi-omics data model constructs a graph structure according to spatial multi-omics data and spatial position coordinates, a graph attention encoder performs cell spatial multi-omics data fusion based on the constructed graph structure, and a decoder performs data reconstruction based on fused data to complete cell spatial multi-omics data integration; and constructing a multivariate loss function to carry out iterative optimization on the cell space multi-omics data integration process of the space multi-omics data model, and outputting an optimal cell space multi-omics data integration result. According to the method, the batch effect is solved, the smoothness of a final result in space and the accuracy of spatial analysis are improved, and the integration of multi-omics data of the cell space is realized.

The invention provides a single-cell multi-omics cell type annotation method based on distribution and knowledge alignment, and belongs to the technical field of single-cell type annotation, the method comprises the following steps: obtaining single-cell transcriptome data and single-cell chromatin accessibility sequencing data, and pre-training and training a multi-omics variation auto-encoder model, the multi-omics variational auto-encoder model is combined with a variational auto-encoder and a knowledge distillation technology, and multi-omics single cell data is integrated and annotated through distribution and knowledge alignment. And performing cell type prediction on the single cell transcriptome data and the single cell chromatin accessibility sequencing data which are input at the same time by using the trained multi-omics variational auto-encoder model. According to the method, the problem of limitation of a method only depending on single omics is solved, the synergistic effect between the omics is enhanced, the accuracy of annotation is improved, and the calculation overhead is reduced through knowledge distillation.

The invention discloses a multi-omics analysis method and device, electronic equipment and a storage medium, and relates to the technical field of biological information, and the method comprises the following steps: obtaining a spatial single cell function map set based on multi-omics data, the multiple omics data at least comprise single cell transcriptome data, spatial transcriptome data, single cell chromatin transposase accessibility sequencing ATAC data and whole genome association research GWAS data, and the spatial single cell function map set comprises a plurality of maps used for indicating spatial distribution of different omics data; and establishing a mapping relationship from the spatial transcriptome data to the single cell transcriptome data, wherein the mapping relationship indicates spatial position information of each cell in the single cell transcriptome data. By utilizing the method, the flexibility during multi-omics analysis is improved, and the data integration capability is 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.

Provided herein are methods, systems, compositions, and kits for detecting and decoding an encoded assay. The present disclosure provides low cost, multiplexed and automatable assays by hypercoding, a scalable technology for detection and quantitation of multi-omics targets. Hypercoding, or coding, utilizes signals from fluorescent hybridization with an error-corrected code to enable accurate detection and high-plexity targets of interest from samples. The present disclosure provides encoded assays for multiplex target detection from a sample suitable for detection by hybridization. Detection polynucleotide complexes, or detection oligonucleotides and anchor oligonucleotides, of the present disclosure are utilized in the detection and decoding of the hypercodes to produce optical signals capable of being decoded by a soft decision decoding algorithm for determining the presence of a target of interest, or multiple targets of interest, in a sample.

The invention discloses a cancer category detection method based on multi-omics data clustering remarking, and relates to the technical field of machine learning. The learning process of the category detection model is as follows: n multi-omics data samples are acquired, each sample comprises m different categories of omics data, sample labels are cancer categories corresponding to the samples, and K cancer categories are provided; preprocessing the sample, and generating sample features for the preprocessed sample by using the model; according to sample features, clustering the preprocessed samples into K clusters; performing impurity measurement on each cluster, and re-marking sample labels in the clusters; performing comparative learning based on a re-marking result to enable the model to generate accurate sample features; and according to accurate sample characteristics generated by the model, predicting cancer types. According to the invention, cancer category detection is carried out by using the category detection model, and the accuracy of cancer typing is improved through reconstruction of the sample labels and application of a contrast learning method.

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 relates to the technical field of gene sequencing, in particular to a silkworm gene sequencing path optimization system and method based on a hybrid parallel genetic algorithm. According to the technical scheme, the method comprises the steps of data preprocessing and feature modeling, hybrid parallel genetic algorithm optimization, dynamic path planning and resource allocation and multi-omics verification and result output. Reference is provided for assembling path planning by recognizing the repeated area, local optimum is avoided by means of the hybrid parallel genetic algorithm, efficient assembling of the high-complexity repeated area is achieved, standardization processing is carried out on data, errors caused by data differences are reduced, and the accuracy of assembling path planning is improved. The multi-omics feedback correction module integrates transcriptome and epigenetic data, corrects an assembly result and inhibits error accumulation, and meanwhile, based on deep Q network model reinforcement learning and a dynamic resource scheduling module, assembly strategies and resource allocation are dynamically adjusted, efficient utilization of computing resources is achieved, and resource waste is reduced.

The invention belongs to the technical field of biological data processing, and discloses a multi-omics data fusion method and system based on a hypergraph network. According to the method, a hypergraph structure is adopted for modeling omics data, high-order interaction information can be more efficiently mined, and a complex association mode between biological entities can be more comprehensively revealed; by introducing a hypergraph aggregation mechanism, high-order complex relationships in various omics data can be represented more accurately, the limitation that a traditional method can only capture the relationship between every two nodes is overcome, and the understanding ability of the model to the interaction between biological entities is improved; according to the hypergraph fusion method, multi-omics specific hypergraphs are effectively integrated, unified representation of cross-omics data is realized, the representation capability of the model for complex multi-relational data is enhanced, and more comprehensive technical support is provided for multiple downstream tasks.

The invention provides an intelligent intervention method for dyslipidemia based on multi-omics analysis, and aims to provide scientific and personalized treatment and lifestyle guidance for dyslipidemia patients. And the predictive ability of the deep learning model is combined with the interpretability and security verification mechanism of the knowledge graph and the rule engine, so that the scientificity, interpretability and security of each intervention scheme are ensured. An intervention scheme is generated through a deep learning model, and meanwhile, a knowledge graph and a rule engine are introduced, so that the interpretability, the safety and the dynamic adaptability of the scheme are ensured. By dynamically analyzing individualized genes, proteins and clinical characteristics, individualized treatment and lifestyle intervention suggestions are provided for patients. The model not only can significantly improve the curative effect and reduce the risk of adverse reactions, but also can realize continuous improvement of an individualized intervention scheme through real-time feedback and dynamic optimization, and provides an efficient blood fat management tool for doctors and patients.

Systems, devices, and methods for processing, analyzing, and classifying biological data sets and generation of cell profiles are provided herein. The data set may include multiple omics data. Some embodiments may include using machine learning to train classifiers of raw multi-omics data and incorporate systematic biological knowledge to understand cellular behavior and cellular status at biomolecular levels.

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 plant phenotype multi-precision information joint prediction method, and relates to the technical field of plant phenotype prediction, and the method comprises the steps: data acquisition and preprocessing; constructing and training a model; through interpretability analysis, the method can be applied to different species and various plant traits and can be expanded to other biological fields; new omics data and analysis functions can be flexibly supplemented, and the future requirements of large-scale multi-omics research are met; compared with a traditional linear or general machine learning method, the deep learning model is better in performance in a complex gene interaction scene, and stable prediction performance is kept.

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 aims to provide a multi-modal cancer prognosis evaluation method based on Transform, which comprises the following steps: S1, pre-processing multi-modal data, carrying out foreground segmentation and patch segmentation on pathology to obtain patch features, and clustering to obtain a phenotype cluster expressed by morphological specificity; performing expression significance analysis on the multi-omics data, and screening significant expression features; s2, single-mode modeling is carried out, strong pathological representation is obtained based on Transform and a two-stage attention convergence layer, and multi-omics modeling is realized based on a self-normalization network (SNN); s3, cross-modal interaction is carried out, and dense omics-phenotype cluster common attention mapping is realized by using a cross-modal Transform; s4, performing multi-modal fusion, and integrating the cross-modal interaction feature, each single-modal feature and clinical information by using a unified Transform coding layer to obtain a final multi-modal representation; and S5, risk prediction: inputting the final multi-modal representation into a risk prediction module to obtain a risk prediction result. Pathology and multi-omics characteristics are effectively fused, and clinical decision making is promoted.

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 relates to a single-cell multi-omics data clustering method based on cross-omics feature fusion, and belongs to the field of single-cell omics analysis of computational biology. According to the technical scheme, the method comprises the following steps: step 1, preprocessing unicellular original data of different omics; step 2, using the preprocessed data to train a plurality of denoising auto-encoders to serve as single view feature extractors of each omics; step 3, training a de-noising auto-encoder as a common view feature extractor by combining the preprocessed different omics data; step 4, utilizing an attention mechanism to enhance public view features; step 5, enhancing cross-omics public representation by using comparative learning; and 6, performing clustering analysis and cell type identification by directly using the fused low-dimensional feature representation obtained by the model. The method is used for solving the problem of clustering analysis of single-cell multi-omics data in the field of bioinformatics, and is particularly applied to multi-omics data fusion and cell type identification.