948 results about "Omics" patented technology

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.

A spatial omics multi-modal fusion method under a single cell level comprises the following steps: extracting spatial morphological characteristics of differential expression genes and cell nucleuses from spatial transcriptome data, single cell sequencing data and histological images, and realizing field adaptation among different platforms by using a conditional variation auto-encoder. And based on a probability inference model, fusing spatial transcriptome expression, unicellular omics and morphological characteristics, and jointly inferring the type and gene expression level of each cell. A spatial cell network is constructed through a graph attention mechanism, and spatial diffusion and recognition of cell types in a full slice range are realized. In combination with a multi-omics enhancement module, undetected gene and protein expression is completed based on expression similarity, and prediction consistency is improved through spatial correction. According to the method, high-resolution reconstruction of single-cell multi-omics information in a three-dimensional space is realized, the information coverage and spatial resolution of spatial omics data are improved, and an efficient and low-cost solution is provided for spatial biology and precise medical research.

The invention discloses a single-cell multi-omics cell type annotation method based on pre-training, and belongs to the technical field of biological information, and the method comprises the following steps: obtaining a gene expression matrix of paired scRNA-seq data and a chromatin accessibility peak matrix of scATAC-seq data, and preprocessing the gene expression matrix and the chromatin accessibility peak matrix, then inputting a pre-trained feature extractor, and obtaining a query feature matrix fused with multiple omics by adopting a multi-head cross attention network of the pre-trained feature extractor; the feature extractor comprises a multi-head cross attention network and two classifiers; and inputting the query feature matrix into a classification model, performing data splitting by adopting a linear layer, then inputting the query feature matrix into a Transform encoder to perform multi-omics genetic feature extraction, and then inputting the query feature matrix into a classifier to perform cell type annotation. According to the cell type annotation method, the problem of deviation of cell type annotation in an existing method is solved.

The invention provides a corn genotype and environment cross-modal feature fusion genome prediction method and model, a deep learning method is adopted to model a complex nonlinear transformation relationship between different omics data including a genome and an environment group, and a GECFGP model is constructed to fuse corn cross-genotype and environment interaction features. Time sequence characteristics and important growth periods related to phenotypes are well mined, and the function of predicting the phenotypes of the complex agronomic traits of the corn is achieved. The method shows high robustness in different experimental settings, has superiority in genome prediction of G * E interaction, explores phenotype-related important growth periods and performance of predicting phenotypes across different years, provides a new thought for a genome prediction analysis method, and has wide application prospects. Therefore, theoretical and application value reference is provided for adaptability analysis of corn in different environments and exploration of key growth periods, and development of the field of intelligent breeding is accelerated.

The invention discloses a multi-omics pathological analysis system and method for colorectal cancer liver metastasis risk prediction, and relates to the technical field of biomedicine and cancer diagnosis, the system comprises a data acquisition module, a data preprocessing module, a cell analysis module, a score construction module, a model construction and training module and a prediction and evaluation module; according to the method, multiple omics data including the single cell data set, the space transcriptome data, the batch RNA-seq data, the clinical data and the full slice image WSIs are integrated, comprehensive and accurate prediction of the colorectal cancer liver metastasis risk is achieved, cross-dimensional data fusion and analysis improve the prediction accuracy and reliability, and the risk of the colorectal cancer liver metastasis is reduced. The invention also provides a powerful tool for deeply exploring the molecular mechanism of tumorigenesis, development and metastasis, and particularly in a cell analysis module, cell subtypes and molecular characteristics closely related to liver metastasis can be disclosed by identifying and analyzing malignant cell subgroups LMTMCs triggering liver metastasis, so that the liver metastasis can be discovered. And a scientific basis is provided for formulating a personalized treatment scheme.

The invention relates to a method for dividing cell types based on single-cell multi-omics data, and belongs to the technical field of bioinformatics. The problems of high noise and high sparsity formed by a common technology are solved. Comprising the following steps: step 1, constructing a deep neural network model, step 2, obtaining a single-cell multi-omics comprehensive data set, step 3, preprocessing the single-cell multi-omics comprehensive data set, step 4, training the deep neural network model based on the preprocessed single-cell multi-omics comprehensive data set, and step 5, training the deep neural network model based on the preprocessed single-cell multi-omics comprehensive data set. And performing cell type division on to-be-detected data based on the trained deep neural network model. According to the method, an scDRMAE model is provided, dependency relationships of different omics are captured through two parallel MAE modules, factors which are usually ignored are considered in cell clustering, interpolation of different omics data is realized by means of a mask prediction mechanism in the cell clustering, and finally, the omics data are effectively fused through an attention mechanism, so that the accuracy of cell clustering is improved. And the accuracy of model cell clustering is improved.

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 an ovarian tumor cross malignancy classification system based on a hybrid radiomics model. On one hand, a backbone network in the Med3D network is used as a depth feature extractor to construct a target network, and multi-modal 3D magnetic resonance image data is used to carry out fine tuning training on the target network to obtain a corresponding ovarian tumor classification network for obtaining depth features and tumor classification results. On the other hand, omics characteristics of the tumor region of interest are extracted, and a tumor classification result corresponding to the omics prediction model is obtained. In addition, the depth features and the omics features are fused, and a corresponding classification result is obtained through a KNN classification algorithm. And finally, obtaining a final ovarian tumor cross malignancy classification result according to weighted average of the three classification results. According to the invention, by integrating various models and algorithms, more comprehensive feature mining and analysis are carried out on the multi-modal MRI image data of the ovarian tumor patient, so that the final ovarian tumor classification effect is improved.

Systems and methods for determining information representing an amount of an indicator of a soil trait based on a machine learning engine, and systems and methods for training the machine learning engine are described. An example method includes obtaining values of a plurality of environmental factors relating to a location, and determining the information representing the amount of the indicator by inputting the values of the plurality of environmental factors into a machine learning engine. The machine learning engine is trained using sample datasets each of which includes values of at least a portion of the plurality of environmental factors of a sample site and information representing an amount of the indicator of the sample site.

The invention discloses a metabonomics data batch correction method based on multi-kernel learning, and belongs to the cross technical field of bioinformatics and analytical chemistry. According to the method, the multi-kernel learning technology is utilized, the advantages of different kernel functions are fused in a self-adaptive mode, a model conforming to data reality is constructed, complex drift characteristics of metabolite signals are accurately captured, and efficient and accurate normalization processing of metabonomics data is achieved. Compared with traditional data standardization methods such as SVR and LOESS, the method has the advantages that the performance is excellent in the aspect of reducing the metabolite peak intensity variability, and the data stability is remarkably improved. In the subsequent multivariate statistical analysis, the classification accuracy is greatly improved, the comparability among different batches of data is also remarkably enhanced, reliable data support can be provided for discovery of disease biomarkers, and the method plays a key role in large-scale metabonomics research.

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 belongs to the technical field of molecular biomedicine, and provides a colorectal cancer early screening method based on multi-omics sequencing, which comprises the following steps: collecting a plasma sample to be detected, collecting sequencing data to be detected, and classifying a colorectal tumor early screening model. The training process of the colorectal tumor early screening model comprises the steps of original sequencing data acquisition, quality control and human reference genome comparison, reference methylation data acquisition, tumor specific region screening, plasma cfDNA sequencing data acquisition, coordinate determination sequencing data acquisition, methylation feature set screening and terminal motif feature screening. And carrying out breakpoint motif feature screening, feature merging and model training. By constructing the colorectal tumor early screening model, the non-invasive colorectal cancer detection method with high cost effectiveness is realized, the economic cost of sequencing is reduced, and the accuracy and sensitivity of detection are remarkably improved.

Disclosed are a disease analysis method, a training method and apparatus of a disease analysis model, the disease analysis method includes: acquiring first omics data and second omics data of a patient, wherein first omics includes a plurality of first loci and second omics includes a plurality of second loci; inputting the first omics data and the second omics data into a fusion algorithm model to obtain a predicted result of the patient, wherein the fusion algorithm model is formed by fusion and construction according to a first omics model and a second omics model, the first omics model is constructed according to sample data of the first omics, and the second omics model is constructed according to sample data of the second omics.

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 discloses a prostate cancer endocrine therapy drug resistance prediction system based on multi-model artificial intelligence. The prostate cancer endocrine therapy drug resistance prediction system comprises a data acquisition and preprocessing module, a single omics prediction module, a multi-modal feature integration module, a model training module and a prediction result output module. Multi-omics data of a patient is collected and preprocessed, data representations of omics data of different labels are generated by using priori knowledge, and an optimal single-omics prediction model is matched for the omics data of different labels based on the data representations after knowledge enhancement. And constructing a multi-omics prediction model of the prostatic cancer endocrine treatment drug resistance by using the optimal single-omics prediction model corresponding to the different label medical data by adopting Stacking, predicting the drug resistance risk of the patient within a preset time, outputting an early warning, and adjusting the treatment scheme of the patient according to the drug resistance risk. According to the method, the multi-omics data are integrated, the accurate multi-omics prediction model for the drug resistance of the endocrine therapy of the prostate cancer is constructed, and the accuracy and clinical applicability of drug resistance prediction are improved.

The invention provides a cancer subtype classification method based on a KAN network and multi-omics data, and the method comprises the steps: obtaining the multi-omics data, including genomics data, transcriptomics data and proteomics data, and constructing a test data set; carrying out feature extraction and integration on the multi-omics data by utilizing a Colmogolov-Arnod network model, and carrying out training to obtain a trained cancer typing model; in the model evaluation stage, a Monte Carlo discarding method is adopted to carry out multiple times of forward propagation so as to estimate the uncertainty of a model prediction result and improve the robustness of the model; and carrying out cancer typing prediction, and carrying out high-precision cancer typing on multi-omics data by using the trained Colmogolov-Arnod network model. According to the method, the problem of heterogeneity of multi-omics data can be effectively solved, the accuracy and robustness of cancer typing are improved, and the effect of personalized treatment is improved.

The invention relates to the technical field of image analysis, in particular to a multi-modal data fusion IDH wild type glioblastoma typing method and system, and the method comprises the steps: obtaining a data set of a GBM patient; based on the data set, radiomics characteristics, pathomics characteristics, whole exon sequencing data, transcriptome sequencing data and proteomics characteristics are obtained; performing multi-modal data fusion on the image omics characteristics, the pathological omics characteristics, the whole exon sequencing data, the transcriptome sequencing data and the proteomics characteristics, and constructing a multi-modal fusion typing framework to identify the MOFS subtype; and based on image omics features, constructing a deep neural network classifier for predicting the MOFS subtype, and verifying the identified MOFS subtype. Three different subtypes in IDH wild-type adult glioma are determined and verified using an artificial intelligence method by integrating multi-mode data from MRI derived radiomics, WSI derived pathomics, WES, RNA sequencing, and MS-based proteomics.

The invention aims to provide a multi-modal cancer prognosis evaluation method based on a graph. The method comprises the following steps: S1, pre-processing multi-modal data, segmenting a pathological tissue part into patches, extracting patch features, and performing surrounding type composition on the patches according to cell migration continuity; the omics data is subjected to GSEA enrichment classification, wherein five cancer development processes are included; s2, single-modal features are obtained, and modal corresponding features are obtained by using a modal enhanced and shared graph mask auto-encoder; s3, cross-modal interaction is carried out, and multi-modal data distribution mapping alignment is realized by using a cross-modal alignment module; s4, performing multi-modal fusion, and fusing the cross-modal interaction feature and each single-modal feature by using a Hyperdge-Mixer module to obtain a modal fusion feature; and S5, risk prediction: inputting the final multi-modal representation into a risk prediction module, and finally obtaining a risk prediction result. The cancer prognosis is realized by effectively fusing pathology, genes and clinical data, an end-to-end prognosis analysis framework is realized by utilizing a deep learning technology, the method has good performance and relatively high interpretability, and the clinical application practicability is enhanced.

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 provides an EGFR wild-type lung adenocarcinoma prognosis risk assessment method based on multi-omics and machine learning, and the method comprises the steps: obtaining multi-omics and clinical data of lung adenocarcinoma, obtaining a data set, and carrying out the multi-omics consensus clustering, and obtaining a molecular typing result; high-risk subtype specific candidate genes are identified, a candidate prognosis gene set is obtained, multi-algorithm machine learning comparison optimization is carried out, and a modeling strategy is obtained; performing feature screening and model training to obtain a multi-omics feature model so as to calculate an individual risk score of the to-be-tested sample; the individual risk score and the clinical staging information are utilized to obtain a clinical column diagram and a survival prediction result, then the flow of the multi-omics feature model, the individual risk score and the survival result is Web to obtain a clinical system, and a lung adenocarcinoma prognosis risk assessment result is output. The invention can realize an objective, accurate, generalizable and multifunctional prognosis evaluation and treatment guidance tool, and has important clinical application value and wide industrialization prospect.

The invention discloses a classification method and a classification device for pancreatic neuroendocrine tumors. The classification method comprises the following steps: using a pre-trained segmentation model to segment a plurality of HE full slice images from a plurality of patients; quantizing each segmented HE full slice image to determine a plurality of first feature parameters; based on the cellular morphology and topological features of each HE full-slice image, extracting a plurality of second feature parameters from each HE full-slice image; for each HE full slice image, screening out a target feature from the plurality of first feature parameters and the plurality of second feature parameters, and calculating a pathological omics score; inputting the pathological omics scores and the conventional pathological features of the plurality of HE full slice images into a logistic regression model for training to obtain a trained logistic regression model; and classifying the HE full slice images to be classified by using the logistic regression model. According to the method, pancreatic neuroendocrine tumor G classification can be quickly and accurately realized at low cost.

The invention discloses a metabonomics data batch effect correction method based on deep learning. The metabonomics data batch effect correction method comprises two stages of intra-batch correction and inter-batch correction. In the correction stage in batches, an SERRF method is adopted for modeling the sampling sequence of the QC samples, the systematic drift effect is recognized and corrected, and data deviation caused by factors such as the sampling sequence in the same batch is eliminated. In the inter-batch correction stage, firstly, the mass center of each batch is calculated, and the pairwise correction sequence is determined based on the distance between the batches; on the basis of a combined framework of a generative adversarial network and an auto-encoder, systematic deviation among batches is reduced through adversarial training, mutual nearest neighbor alignment loss is introduced, and biological information loss caused by over-correction is avoided. According to the batch correction method of the multi-batch data pairwise correction sequence, the advantages of the random forest and the deep adversarial alignment network are combined, and a two-stage joint correction framework is constructed. While real metabolic differences of biological samples are reserved, errors in data are eliminated, and reliable data support is provided for metabonomics research.

The invention relates to a method for acquiring a biomarker for assisting CRLM early diagnosis. The method comprises the following steps: acquiring sequencing data, clinical information and metabonomics characteristics of a CRC sample; evaluating and determining a plurality of differentiated machine learning models; performing classification according to the determined machine learning model, and constructing a CRLM biomarker prediction model; screening candidate biomarkers according to the prediction model; and verifying the candidate biomarker at least according to the tissue slice and the serum sample, and determining a final biomarker.

Disclosed herein is a system for detection of a molecular community in a molecular network, the system comprising an information handling system that comprises at least one processor; and a non-transitory storage medium storing instructions readable and executable by the processor to perform a method comprising receiving molecular data from an analytical device and / or a data repository; generating an unpruned molecular network from the molecular data; treating the unpruned network with a community detection algorithm to form one or more molecular communities; parsing the molecular communities to prevent the formation of singletons; and identifying at least one molecule present in the molecular community by comparing the molecular community with reference spectra from a library of spectra or by using a molecular structure prediction tool.

The invention discloses a gene identification method based on a mask graph auto-encoder. The method comprises the following steps: 1, acquiring gene interaction data and omics characteristic data and preprocessing the data; 2, masking the processed data, specifically comprising two branches, a node masking module and an edge masking module; 3, inputting the masked network into a graph automatic encoder for training, wherein the graph automatic encoder learns embedded representation of the network by reconstructing nodes and edges of the network; and 4, obtaining low-dimensional embedding of the features through the trained encoder, and finally performing gene classification by using a logic regression classifier. According to the method, the node information and the structure information of the graph are concerned at the same time by respectively masking the nodes and the edges in the network, and the dependence of the feature training model on the label information is reduced in a self-supervised learning mode, so that the genes can be accurately classified.

The invention relates to a multi-modal survival prediction method fusing a digital pathological image and omics data. The method comprises the following steps: acquiring a full-view digital pathological image and omics data of an esophageal squamous cell carcinoma patient; carrying out preprocessing and feature extraction on the full-view digital pathological image and the omics data to respectively obtain a pathological embedding representation and an omics embedding representation; fusing the pathological embedding representation and the omics embedding representation by using a multi-head attention mechanism of Transform to obtain a preliminary fusion feature; performing deep interaction on the pathological embedding representation and the preliminary fusion feature to obtain a final fusion feature; and predicting a survival result based on the final fusion feature. According to the method, bimodal sharing of pathological images and omics data and full fusion of features are realized, and prognosis survival prediction of esophageal squamous cell carcinoma cancer patients can be better realized.

The invention provides a spatial omics data completion method and system based on a variational graph auto-encoder, and relates to the technical field of bioinformatics. Extracting partial common genes in the data pair to obtain a feature matrix; based on the spatial position information, obtaining a first sub-adjacency matrix of cells in the spatial transcriptomics sequencing data; respectively obtaining a second sub-adjacency matrix of the cells in the single-cell RNA sequencing data and a third sub-adjacency matrix of the cells in the data pair based on gene expression similarity; combining the first sub-adjacency matrix, the second sub-adjacency matrix and the third sub-adjacency matrix to obtain a final adjacency matrix; and inputting the feature matrix and the final adjacent matrix into a pre-trained variational graph auto-encoder network to complete the deletion gene of the space transcriptomics. According to the method, the position information and gene expression characteristics between idle data cells can be effectively utilized, errors can be reduced, and meanwhile, the similarity between complementation genes and true values can be improved.