795 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 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 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.

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 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 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 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.

The invention discloses a diagnosis model for hepatocellular carcinoma microvascular invasion and a construction method and application thereof, and belongs to the technical field of medical examination. Metabolite in the blood of a hepatocellular carcinoma patient is taken as a cut-in, preoperative clinical tumor indexes are combined, it is found that PIVKA-II, GGT, L-Serine, Sarcosine and tumor sizes have important significance on formation of hepatocellular carcinoma MVI, a diagnosis model based on multiple omics and multiple dimensions is established based on the diagnosis indexes, the diagnosis model is used for diagnosis of the preoperative MVI of the hepatocellular carcinoma patient, and the diagnosis model is used for diagnosis of the preoperative MVI of the hepatocellular carcinoma patient. The defects of existing diagnosis means in the aspect of preoperative diagnosis are overcome, and a more timely and accurate basis is provided for clinical decision making.

The invention relates to the technical field of bioinformatics analysis, in particular to an enhanced CT image-based hepatocellular carcinoma postoperative early recurrence multi-modal prediction model and a construction method thereof, and the construction method comprises the following steps: S1, data acquisition: collecting clinical data and liver enhanced CT images of a hepatocellular carcinoma surgical patient, and evaluating the recurrence condition of the HCC patient; s2, image preprocessing: sketching a region of interest in the image, and extracting deep learning features; s3, establishing a model: establishing a plurality of image omics models, constructing a plurality of deep learning models, and constructing a multi-modal model; s4, evaluating and verifying the model: evaluating the prediction accuracy of the multi-modal model by adopting an ROC curve and an AUC value, and evaluating the prognosis prediction capability of the patient by using a Kaplan-Meier curve; according to the method, the characteristic expression ability and the recurrence risk discrimination performance are improved, a scientific basis can be provided for follow-up visit and treatment of the hepatocellular carcinoma postoperative patient, and the lifetime of the patient is prolonged.

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 relates to the field of forensic medicine, and discloses a method and system for identifying smoking behaviors and application of the method and system. The construction method comprises the following steps: respectively obtaining saliva and excrement of a smoking object and a non-smoking object; performing metagenome sequencing to obtain original microbiome data; after processing the original microbiome data, extracting multi-omics feature information, including species information and gene function annotation information; based on a statistical analysis method, carrying out diversity analysis and screening microbial markers and functional genes significantly related to smoking behaviors; constructing a training set and a test set, and training a machine learning model and model evaluation by taking the microbial markers and / or the functional genes as input features; and performing smoking behavior identification on a target sample by using the model, and outputting a prediction result. The accuracy rate of model identification reaches 0.7966, the sensitivity is 0.8750, the specificity is 0.7037, and the method can be applied to health assessment and judicial expertise.

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 relates to a method and a system for simultaneously acquiring complete form and omics molecular information of a single neuron. The method comprises the following steps: acquiring fluorescently-labeled samples of cell bodies, dendrites and axons of the neuron; performing cyclic operation of scanning imaging and cutting on the section of the sample until the scanning imaging of the whole sample is completed, and collecting a section containing a fluorescence-labeled neuron cell body obtained by cutting; the method comprises the following steps: reconstructing a sample section three-dimensional image obtained based on scanning imaging operation to obtain a complete form of neurons, extracting fluorescently-labeled neuronal cell bodies from collected slices, and obtaining omics molecular information of the neurons through omics analysis. According to the method, the fine and complete form and omics molecular information of the same neuron can be effectively integrated, and the problem that the two kinds of key information cannot be obtained at the same time at the single neuron precision level is successfully solved.

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.

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 provides a gene module analysis method and device, equipment and a storage medium, and relates to the technical field of space transcriptomics. The method comprises the steps of obtaining space-time group chip expression data, performing linearization processing, and performing sliding window cutting to obtain a plurality of window units arranged according to a space sequence; calculating gene function activeness and constructing a matrix; clustering and grouping are carried out to obtain window groups; performing difference analysis screening on the window groups to obtain a difference expression gene set; and performing gene module analysis to obtain a functional gene module. According to the method, through an integrated processing flow of structure linearization, sliding window cutting, activeness quantification, clustering analysis and module identification, fine extraction and function reconstruction of spatial expression information in the curled tissue are realized under the condition that an additional algorithm or external data is not introduced; and the practicability and research value of the spatial omics data in a complex organization structure are improved.