3483results about "Hybridisation" patented technology

The invention discloses a genomics-based Parkinson's disease drug target prediction model construction method, and relates to the technical field of drug research and development, and the method comprises the following steps: collecting genomics, transcriptomics and proteomics data related to Parkinson's disease patients, and carrying out quality control and standardization processing; through differential expression analysis and function enrichment, key genes and signal pathways related to Parkinson's disease are identified, and a potential drug target range is determined. By integrating genomics, transcriptomics and proteomics data of patients with Parkinson's disease, molecular mechanisms related to Parkinson's disease can be comprehensively analyzed, multi-target combination is optimized in combination with the ant colony algorithm, the limitation that a traditional single-target model is difficult to capture complex disease network comprehensiveness is effectively overcome, and the method is suitable for popularization and application. The accuracy of target spot prediction is remarkably improved, a more reliable action target spot is provided for drug research and development, and the failure rate of clinical tests 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 provides a protein methylation site prediction method fusing quantum and region perception, which comprises the following steps: step 1, acquiring a protein sequence as a data source, and respectively constructing a training set and an independent test set; 2, constructing a multi-modal feature for each protein sequence by adopting a three-way nested scattering network, and fusing the multi-modal features to obtain an optimized fusion feature tensor; and step 3, inputting the optimized fusion feature tensor into a RaQMeNet network model, and performing a methylation site prediction task. The performance indexes of the method are greatly superior to those of the prior art, and the method has higher adaptability, stability and interpretability, can be widely applied to a plurality of bioinformatics and biological medicine related fields such as protein function annotation, disease mechanism research and drug target discovery, and has good application prospects and commercial values.

The invention discloses a transcriptomics spatial domain identification method, and belongs to the technical field of transcriptomics. The objective of the invention is to solve the problems of low data noise reduction precision and poor recognition effect of an existing spatial transcriptional spatial domain recognition method. The method comprises the following steps: firstly, obtaining an undirected neighborhood graph according to a gene expression matrix, obtaining embedded representation of the gene expression matrix by utilizing an encoder, obtaining a corresponding reconstruction matrix by utilizing a decoder, and further determining reconstruction loss; meanwhile, a ZINB model is used for fitting a reconstruction matrix, and a ZINB loss function is obtained; then, an augmented graph is constructed based on the undirected neighborhood graph, respective embedded matrixes are obtained through an encoder, the comparison loss of the undirected neighborhood graph and the comparison loss of the augmented graph are obtained through a comparison representation learning mechanism, and then the neighbor comparison loss is obtained; total target loss is obtained based on all losses, a joint optimization strategy is adopted for training, and after training of the whole model is completed, dimensionality reduction and spatial domain recognition are carried out on a generated reconstruction matrix.

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.

Methods of deconvolving a feature profile of a physical system are provided herein. The present method may include: optimizing a regression between a) a feature profile of a first plurality of distinct components and b) a reference matrix of feature signatures for a second plurality of distinct components, wherein the feature profile is modeled as a linear combination of the reference matrix, and wherein the optimizing includes solving a set of regression coefficients of the regression, wherein the solution minimizes 1) a linear loss function and 2) an L2-norm penalty function; and estimating the fractional representation of one or more distinct components among the second plurality of distinct components present in the sample based on the set of regression coefficients. Systems and computer readable media for performing the subject methods are also provided.

Platforms, systems, and methods for genetic generalization in synthetic biology development. According to one aspect, there is provided a method for predicting performance associated with genetic edits, the method comprising: receiving, by a platform, information about a strain of a microorganism, wherein the information about the strain comprises information describing a plurality of genetic edits to a base strain of the microorganism; generating, by the platform, a set of genetic embeddings based on the information about the strain, wherein the generating comprises processing the information about the strain using one or more embedding models, wherein each of the one or more embedding models: receives the information about the strain of the microorganism as input; and applies computational transformations to the input using a corresponding embedding model to generate a multi-dimensional vector representation for each of the plurality of genetic edits.

The invention discloses a rice nitrogen response regulation and control network analysis and breeding target identification system and method based on multi-omics data. According to the system, organic combination of regulation and control network construction based on single or multiple varieties of materials, key transcription factor recognition and accurate positioning of regulation and control areas where transcription factors play roles is achieved through an expression-chromatin accessibility correlation research method, and cis-trans effect distinguishing of the regulation and control areas is achieved through a deep learning model. The method comprises the following steps: carrying out nitrogen starvation pretreatment on rice, then carrying out nitrogen resupply, collecting a root sample, and carrying out ATAC-seq and RNA-seq sequencing; an eCAAS method is adopted to construct a regulation and control network, and key transcription factors are identified and accurately positioned; the chromatin accessibility difference of different varieties is predicted through a deep learning model, the cis-action effect and the trans-action effect are distinguished, an upstream transcription factor target is provided for genes dominated by the trans-effect, and haplotype and editable regulatory region targets available for direct breeding are provided for genes dominated by the cis-effect.

The invention relates to the technical field of bioinformatics, and provides a visual analysis method and system for a rice multi-tissue single cell expression profile. The method comprises the following steps: comparing sequencing data of an original single cell transcriptome of a rice tissue to obtain a standardized transcriptome data set; performing batch effect correction and integration on the standardized transcriptome data set to obtain a whole plant expression matrix; performing cell type annotation on the whole plant expression matrix to obtain a cell type annotation system; carrying out visual dimension reduction processing on the whole plant expression matrix fused with the cell type annotation system, and carrying out co-expression network construction to obtain a modular tissue correlation analysis model; and establishing an interaction end based on the module organization correlation analysis model, and realizing data visualization analysis through the interaction end. The invention provides a one-stop analysis platform for rice cell heterogeneity research, functional gene mining and molecular breeding.

The invention discloses a system and a method for predicting postoperative recurrence risk after triple negative breast cancer neoadjuvant therapy based on multi-modal time sequence medical image data, and belongs to the field of medical image analysis. The system comprises a data processing module used for constructing a multi-modal data set; the multi-modal feature extraction and screening module is used for extracting deep learning, radiomics and tumor habitat features from the region and carrying out feature screening; the model training module is used for constructing a time sequence model based on a Transform architecture and carrying out training through a multi-task learning strategy integrated with time consistency constraint and gene association auxiliary loss; and the recurrence risk prediction module is used for loading the trained model and outputting a recurrence probability and a risk level. According to the method, the multi-modal time sequence image and gene information are fused, so that the recurrence risk of the triple negative breast cancer patient is dynamically and accurately quantified, and support is provided for clinical individualized treatment decision.

The invention relates to a method for identifying a disease microorganism-metabolite combined marker based on a host-flora co-metabolism model. The method comprises the following steps: processing microbiome data; setting diet constraint conditions; determining classification composition of intestinal microorganisms of each individual, mapping abundance of each microorganism to a human microorganism genome scale metabolism model, and constructing an individualized flora metabolism network model; carrying out metabolic flow simulation on the constructed flora metabolism network model; integrating the constructed flora metabolism network model with the host metabolism network model, and predicting the metabolic flow and metabolite change of the whole system; and establishing a disease classifier based on a random forest machine learning algorithm to identify the microorganism-metabolite combined marker. According to the method, the complex metabolic process between the human body and the intestinal flora is restored in a metabolic network modeling and metabolic flow simulation mode, the method has the non-invasive characteristic, the time and economic cost is remarkably reduced, and meanwhile, the method has a great advantage in accuracy.

The invention belongs to the field of bioinformatics, and relates to an anti-cancer drug response prediction method based on an attention mechanism. The method comprises the following steps: firstly, capturing uniform-dimension drug and cancer cell line characteristics through a multi-layer perceptron; secondly, fusing drug characteristics by adopting a Transform encoder, and constructing a cell encoder for cancer cell line characteristic polymerization; then, designing a cross-modal cross fusion module to promote information interaction between the two; and finally, predicting a semi-suppressed concentration value subjected to logarithmic transformation between the two through a multi-layer perceptron. Experimental results show that compared with an existing optimal method, the method has the advantage that the RMSE is reduced by 2.9%. According to the method, accurate prediction of the anti-cancer drug response is achieved by integrating drug and cancer cell line data, screening of potential anti-cancer drugs can be accelerated, personalized treatment schemes can be optimized, the cure rate of cancer patients is further increased, and the method has great significance in cancer treatment.

The invention provides a construction method of a predictive cell senescence model, a method for predicting the senescence state of a tissue sample based on the senescence model, a method for screening potential therapeutic drugs, equipment, a medium and a program product, and relates to the field of intelligent medical treatment. The model construction method comprises the following steps: acquiring a training set sample expression profile data set; identifying a key senescence gene set from the data set by using a feature selection algorithm; inputting the key senescence gene set into a machine learning model to fit a prediction model, and determining an optimal hyper-parameter to obtain a cell senescence model containing the weight of a single gene in the key senescence gene set; the cell senescence model is a senescence score obtained by calculating the sum of the product of the expression quantity of a single gene and the regression coefficient thereof. The cell senescence model, namely PreCSenM, is constructed by integrating a plurality of senescence characteristic gene sets and a gene scoring algorithm, the accuracy in CS evaluation is superior to that of 10 existing methods, and the application of CS from biological research to clinical scenes is also realized.

The invention discloses a non-paired single cell multi-omics data gene regulatory network inference method, which comprises the following steps: collecting single cell sequencing non-paired data, and preprocessing the data; inputting the pre-processed single cell sequencing non-paired data into a multi-layer variational auto-encoder structure to generate advanced potential variables and reconstructed single cell sequencing non-paired data; training a layered GAN by adopting a layered adversarial alignment mechanism according to the advanced potential variables and the reconstructed single cell sequencing non-paired data; according to the advanced potential variables, adopting a mutual nearest neighbor strategy to train a mutual nearest neighbor module; fusing the advanced potential variables of different modes by adopting a gating fusion mechanism, constructing a unified advanced potential variable, and completing adaptive feature integration of the multi-mode advanced potential variables; according to prior information, an initial adjacency matrix is established, a gene regulation network is constructed in combination with unified advanced potential variables, and the gene regulation network with biological rationality is directly deduced from non-pairing input.

The invention discloses a universal nucleic acid probe design method based on hybridization thermodynamics and kinetics, which comprises the following steps: 1) determining a target amplification region according to a fragment interval, generating a primer pair which meets the length requirement and covers a target fragment, calculating the Gibbs free energy change of nucleic acid hybridization by adopting an intelligent nucleic acid hybridization algorithm, and calculating the Gibbs free energy change of the target fragment according to the Gibbs free energy change; by combining chemical thermodynamics, kinetic parameters, sequence complementarity and enzymatic reaction characteristics, generating an optimal primer pair under a multi-dimensional constraint condition; and 2) on the basis of the generated optimal primer pair, generating a probe combination meeting a free energy difference threshold as a candidate pool, screening the probe combination with the highest objective function value from the candidate pool through an iterative process, and updating the candidate pool step by step until the primer pair and the probe combination with the highest objective function value are output. By introducing an intelligent algorithm and combining a thermodynamic model, a dynamic model and specificity evaluation, an optimal primer and probe combination is automatically generated, and the stability and reliability of design are ensured.

The invention discloses a gastric cancer multi-omics marker detection method, system and device, and the method comprises the following steps: S1, collecting a public database open-source space transcriptome, a single cell sequencing sample and bulk-RNAseq data for pre-processing, and collecting a primary tissue sample of a gastric cancer patient in the center for data processing; s2, integrating different modal data samples to obtain a patient label of an input end, and constructing a marker detection model and training the marker detection model; and S3, extracting a target feature value from the input external pathological section by using the trained model, and generating a diagnosis prediction result. Through multi-modal data chimerism, algorithm optimization and AI system development, subpopulation cell marker proportion prediction and prognosis diagnosis and marker evaluation with population prognosis information are realized, and an integrated diagnosis scheme for breaking through molecule-space-prognosis information is constructed.

The invention provides a drug relocation method and a drug relocation system. The method comprises the following steps: predicting an expression profile of a drug after cell line disturbance according to a chemical structure of the drug, dose information of the drug and an undisturbed expression profile. And calculating the differential expression profile of the gene in the cell line based on the expression profiles before and after the cell line is disturbed by the drug. And for each drug, according to the differential expression profiles of the genes, calculating an average value of the differential expression profiles of the genes after the drugs disturb different cell lines, and sorting the genes based on the average value to obtain a sorting list of the differential expression profiles of the drugs. And according to the gene characteristics of the target disease and the sorting list, calculating the enrichment score of each drug on the target disease, and according to the enrichment score, evaluating the potential efficacy of the drug on the target disease. The drug relocation method provided by the invention can be used for giving disease specific gene characteristics for drug library screening.

The invention discloses a method for identifying the effectiveness of a Cas target spot by combining a gene large model and ML, and belongs to the field of biotechnology and artificial intelligence. The method comprises the following steps: constructing a detection array of A types of combined sequences and targeted combined sequences, wherein the detection array is provided with A detection units; respectively adding a combined sequence into each detection unit, carrying out incubation reaction, obtaining a fluorescence value of each detection unit, and carrying out normalization processing; carrying out key feature extraction on each combined sequence by adopting a feature extraction model; constructing a label set and a training set, constructing an integrated model, and training by adopting the label set and the training set; forming a machine learning model by the feature extraction model and the trained integrated model; and combining the new target sequence and the PAM sequence into a to-be-analyzed combined sequence, inputting the to-be-analyzed combined sequence into the machine learning model to obtain a predicted fluorescence value, and judging the recognition capability of the Cas protein on the combined sequence based on the fluorescence value. The method can assist in targeted therapy detection.

The invention discloses a high-activity enzyme, a screening method and application, which combine far-end potential site saturation test and iterative screening to accelerate the development of high-thermal-stability and high-activity bio-enzyme and effectively solve the problems of complicated screening steps and limited screening range of the existing high-activity bio-enzyme. The mutant obtained through strategy transformation can be used for efficiently synthesizing pharmaceutical chemicals and has relatively high industrial production application value.

The invention belongs to the technical field of bioinformatics, particularly relates to a computer simulation and regulation method and system for beef cattle fat metabolism, and aims to solve the problems of excessive fat deposition, low feed conversion efficiency, difficulty in multi-objective optimization and the like in existing beef cattle breeding. A multi-scale biological network model is constructed, a computer simulation and prediction engine is developed, an intelligent intervention strategy optimization engine is designed, and implementation and feedback optimization are carried out. By reducing unnecessary fat deposition, improving the feed conversion efficiency and improving the beef quality, the economic benefit of beef cattle breeding is remarkably improved, the requirements of consumers for high-quality and safe beef products are met, and the method has important value for promoting intelligent upgrading and sustainable development of the beef cattle industry.

The invention discloses an mRNA non-coding region sequence optimization method and system and electronic equipment, and the mRNA non-coding region sequence optimization method comprises the steps: constructing an initial candidate library according to a target protein; inputting the initial candidate library into a pre-trained mRNA sequence optimization model to obtain a prediction data set; performing multi-dimensional scoring and sequence optimization on the prediction data set to obtain a sequence recommendation group; performing biological verification on the sequence recommendation group to obtain an optimized mRNA sequence; wherein the prediction data set comprises a sequence ID, a sequence content, a prediction TE score and a confidence interval. According to the method, the translation efficiency of the mRNA sequence can be efficiently and accurately predicted, the candidate sequence with high expression potential is screened out, meanwhile, the consumption of computing resources is reduced, and the overall design cost is reduced.

Processes to spatially align single cells to yield a specimen map with single cell resolution are provided. Methods can perform spatial omics on a specimen to yield spatial omics data. The spatial omics data can be used in combination with single cell omics data to assign single cells to spatial coordinates to yield a resolved specimen map.

The invention relates to the technical field of cell data analysis, and discloses a single-cell transcriptome data and text description conjoint analysis method based on a multi-modal language model, which comprises the following steps: acquiring a single-cell RNA sequencing expression matrix and a corresponding cell text description, preprocessing the single-cell RNA sequencing expression matrix and the corresponding cell text description, and analyzing the single-cell transcriptome data and the corresponding cell text description; according to the method, a multi-modal data set is constructed, deep fusion of gene expression data and text knowledge is realized by constructing a double-model and cross-modal projection module, limitation of a single mode is avoided, a gene expression value and an index sequence are reserved during preprocessing, a rough coding mode is changed, and the cell type identification accuracy is improved; based on a pre-training strategy of comparative learning, matching learning and a cross-modal projection module, fine-grained cross-modal information interaction and sharing are realized, and cross-modal task effects of text generation cells or cell generation texts and the like are optimized.

The invention provides a drug screening and curative effect evaluation method and system based on a glioma organ-like model, and relates to the technical field of biological material analys.The method includes the steps that a three-dimensional fluorescence image of the glioma organ-like model is collected, signal intensity analysis and compensation processing are carried out, a growth feature mapping model is constructed, and the three-dimensional fluorescence image of the glioma organ-like model is obtained; and phenotypic characteristics for representing the glioma organoid heterogeneity are obtained. Inputting the phenotypic characteristics into a drug response evaluation network, and calculating a drug sensitivity score; the drug molecular feature library and the drug collaborative analysis model are combined, the synergistic interaction index of different drug combinations is calculated, the optimal drug combination is screened, the drug administration dosage and the drug administration time sequence are determined in combination with phenotypic features, and finally an individualized treatment scheme is generated. According to the invention, the curative effect of the drug on the glioma organs can be effectively evaluated, and the optimal drug combination and administration scheme can be screened out, so that the accuracy and effectiveness of glioma treatment are improved.

Methods and systems disclosed herein can improve inference of gene expression using cell-free DNA fragments. In an aspect, the present disclosure provides a computer-implemented method for inferring gene expression, the method comprising: obtaining a biological sample from a subject; extracting cell-free deoxyribonucleic acid (cfDNA) from the biological sample, wherein the cfDNA comprises a plurality of cfDNA fragments; performing a sequencing assay on the plurality of cfDNA fragments to generate a plurality of cfDNA sequencing fragments; computer processing the plurality of cfDNA sequencing fragments; and calculating, based at least in part on the computer processing, a gene expression score for a gene in a plurality of genes, wherein the gene expression score indicates a probability of expression or non-expression of the gene in the plurality of genes.

The invention provides a method and a system for evaluating the biological health condition of cultivated land soil based on exploratory factor analysis, and relates to the technical field of evaluation of the biological health condition of soil. The precise analysis of the complex interaction relationship between the soil microorganism function and the environmental factor is realized; through multi-dimensional index fusion and deep mining of a factor model, key biological function factors are effectively discriminated and quantified, and the scientific evaluation capability of soil ecological functions is improved; in combination with causal path analysis, the scheme can reveal the influence of environmental pressure on a microbial regulation and control mechanism, and intelligent attribution and traceability of abnormal health conditions are realized; based on dynamic fusion and grading judgment of function and environment scores, the scheme supports accurate health condition evaluation and hierarchical intervention, the early warning capability and decision scientificity of soil management are enhanced, and the agricultural sustainable development guarantee level is remarkably improved.

Systems and methods for computer modeling in medicine. A sort of period table of medical models is described for personalized diagnostics, prognostics and therapeutics, including at least 80 major categories of medical models. Generative artificial intelligence and geometric deep learning techniques, and algorithms including 2D and 3D graph machine learning and GenAI algorithms, are described, tailored and applied to diagnostic disease description, prognostic prediction and therapeutic development and management, including generation of novel synthetic drugs. The AI and machine learning techniques and algorithms are applied to understand each individual's genetic, RNA and protein anomalies that represent the source of many unique patient diseases. AI-enabled software agents assist physicians and researchers in building patient medical models. Several personalized medicine applications of individualized medical modeling include cardiovascular disease, cancer, neurological disorders, immune system disorders and genetic diseases.

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 relates to the technical field of collaborative prediction, in particular to a plant polygene stress resistance collaborative prediction method and system based on a federal map neural network. The method comprises the following steps: modeling a multilayer heterogeneous graph according to acquired plant genome data; constructing a dynamic graph neural network model based on time sequence perception; training a dynamic graph neural network model by using the multi-layer heterogeneous graph, and performing distributed privacy calculation on the multi-layer heterogeneous graph by using a federated graph neural network; generalization training is carried out on the small sample scene data based on multi-task learning and a cross-species migration mechanism; and obtaining a gene function prediction result. Through a multi-layer heterogeneous graph fusion technology and a dynamic graph neural network design of time sequence perception, multi-dimensional biological information such as gene regulation, protein interaction, metabolic pathways and stress response can be captured at the same time, and time sequence change characteristics of gene expression in the plant stress response process can be captured at the same time.

The invention provides a cell specific transcription factor regulatory network analysis method and a visualization platform, and relates to the technical field of bioinformatics, the method comprises the following steps: constructing a gene regulatory network through a GRNBoost2-cisTarget-AUCell-Cell GRN workflow based on a transcription factor in combination with a motif database; screening a direct regulation relationship in combination with the database, and calculating an activity score of a regulator in each cell; based on activity scores and cell type annotation results, grouping the single cell data by using a unified manifold approximation and projection (UMAP) dimensionality reduction method and a Leiden clustering algorithm, and displaying the following results through an interactive visualization tool: a cell clustering UMAP graph, performing color marking according to cell types; a UMAP graph and a heat map of transcription factor regulator activity; according to the visual map of the gene regulation and control network, transcription factors and target genes are distinguished through node shapes, and regulation and control relations are marked through line weights and colors. According to the invention, an accurate regulation and control network can be provided.