698 results about "Transcriptome" patented technology

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

The invention provides a spatial domain identification method based on data interpolation and cell type deconvolution, and belongs to the technical field of bioinformatics. In order to solve the problems that gap information between adjacent points cannot be utilized in low-resolution spatial transcriptome data and prior information of cell types in a tissue space structure level cannot be fully integrated in a traditional method, the method comprises the following steps: acquiring a spatial transcriptome data set and a single-cell RNA sequencing data set, and performing data preprocessing on the acquired data sets; and carrying out data interpolation on the preprocessed spatial transcriptome data, and carrying out cell type deconvolution in combination with single-cell RNA sequencing data. And constructing a deep learning model based on the graph convolutional network. And training a deep learning model according to gene expression information, spatial position information and cell type information of the spatial transcriptome data after cell type deconvolution by using a self-supervised contrast learning strategy. And performing spatial domain identification on the to-be-detected data based on the trained model.

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 discloses a spatial domain identification method and device based on multi-modal topological consistency, and belongs to the field of transcriptome spatial domain identification, and the method comprises the steps: constructing a multi-layer network which is in one-to-one correspondence with modal information contained in a biological tissue based on spatial transcriptomics data of the biological tissue; extracting a consensus structure feature shared by the multi-layer network and a specific structure feature specific to each layer of network; constructing a cell consistency network of the biological tissue according to the consensus structural features, the specific structural features and the multilayer network; and performing clustering processing on the cell consistency network to obtain recognition results of different spatial domains in the biological tissue. According to the method, the heterogeneity problem among different modal data can be overcome, and the spatial domain recognition effect is better.

The invention discloses a drug perturbation diffusion prediction method fused with cell description, and relates to the technical field of drug perturbation prediction.The method comprises the steps that firstly, a cell perturbation transcriptome database is preprocessed, and a cell-drug combination containing drug characteristics, cell line gene expression and cell line description characteristics is obtained to serve as training data; and then constructing a drug disturbance prediction diffusion model based on cell description, training the drug disturbance prediction diffusion model by using the training data, and finally inputting Gaussian white noise, cell line gene expression before disturbance, drug characteristics and cell line description characteristics into the trained model to predict cell line gene expression after drug disturbance. According to the method, cell line description characteristics are introduced, so that the perception capability of the model on intercellular biological differences is enhanced, and the generalization performance of cross-drug and cross-cell lines is improved.

The present disclosure, in some aspects, provides one or more biomarkers (e.g., plasma biomarkers or circulating biomarkers) for Facioscapulohumeral muscular dystrophy (FSHD). In some embodiments, a biomarker (e.g., plasma biomarkers or circulating biomarkers) described herein is regulated by DUX4. Methods (e.g., non-invasive methods) of using the biomarkers, e.g., for detection (e.g., early detection) and monitoring patient as well as treatment efficacy are also provided. The present disclosure, in other aspects, provides one or more biomarkers (e.g., transcriptome biomarkers) for Facioscapulohumeral muscular dystrophy (FSHD). In some embodiments, a biomarker (e.g., transcriptome biomarkers) described herein is regulated by DUX4. Methods of using the biomarkers, e.g., for detection (e.g., early detection) and monitoring patient as well as treatment efficacy are also provided.

The invention discloses an inference method and an inference system of a gene regulatory network. The inference method comprises the following steps: acquiring transcriptome data and prior gene network data of a single cell; extracting a gene sub-network related to the transcriptome data from the prior gene network data; obtaining a first view and a second view for the gene sub-network according to a first random deletion strategy and a second random deletion strategy; providing the first view and the second view to a neural network model, and performing comparative learning based on the neural network model to obtain incoming features and outgoing features of each node in the gene sub-network; and constructing a regulation score matrix based on the incoming features and the outgoing features, wherein the regulation score matrix displays the regulation association degree between genes in the transcriptome data of the single cell. According to the technical scheme, the direct causal relationship and the indirect association relationship can be effectively distinguished, so that the gene co-expression network more accurately reflects the real regulation relationship.

The invention relates to the technical field of image analysis, in particular to a targeted drug curative effect prediction method based on image recognition, which comprises the following steps: acquiring tissue images and nuclear morphological parameters by a microscope, establishing a database in combination with transcripts, extracting an injury area, recognizing image features through a convolutional neural network, and constructing a prediction model; and inputting candidate drug molecular structures for molecular docking, calculating a repair progress by combining animal verification to establish a curative effect model, predicting drug scores and response time based on the curative effect model to generate a ranking list, screening high-score drug cells, verifying monitored survival, comparing, predicting and outputting a result. The method comprises the following steps: extracting a cell nucleus form, revealing a relation between damage and molecular abnormality in combination with a transcriptome, identifying a target spot corresponding to an abnormal mode and pathological change through deep learning, performing affinity prediction and animal verification on a drug structure, quantifying the repair progress by adopting image difference, and evaluating the curative effect with two dimensions of structure and function. And curative effect scores and response prediction are output to realize system sequencing, so that drug screening is more accurate and practical.

The invention discloses a prediction marker and a prediction method for acute upper respiratory infection susceptibility. The prediction marker and the prediction method are used for solving the problem that current acute upper respiratory infection susceptibility risk prediction is low in accuracy. The method comprises the following steps: firstly, providing a prediction marker for acute upper respiratory infection susceptibility, wherein the prediction marker is a prediction feature combination obtained by comprehensively considering human health state blood routine data and baseline blood transcriptome data and carrying out feature processing; secondly, providing a prediction method, and obtaining a to-be-detected blood sample; and performing blood analysis based on a to-be-detected blood sample, inputting the obtained blood routine sample and the gene expression sample into a susceptibility prediction model pre-trained based on a prediction marker for susceptibility risk judgment, and obtaining a susceptibility risk assessment result of acute upper respiratory infection of a human body corresponding to the to-be-detected blood sample in a future period of time, therefore, the acute upper respiratory infection susceptibility risk of the human body in a period of time in the future can be simply, quickly and accurately predicted.

The invention discloses a single cell space transcriptome data analysis method, device and system, and a storage medium. The method comprises the following steps: acquiring single cell space transcriptome data; the spatial transcriptome data comprises a gene expression map and spatial site coordinates; constructing a graph structure considering gene expression similarity and spatial position continuity on the basis of the data, and performing data representation on a gene expression graph by using an adversarial auto-encoder; in combination with a graph neural network, robust potential characterization is learned, training loss is constructed through reconstruction of a gene expression map, and meanwhile, a clustering prediction result of spatial sites is obtained in combination with mcluster clustering. By adopting the technical scheme of the invention, the spatial clustering analysis with higher precision can be realized, and the key spatial functional region in the biological tissue can be identified.

The invention discloses a spatial transcriptome data spatial domain identification method based on multi-space self-supervised contrast learning, and the method comprises the steps: carrying out the modeling to generate a spatial neighborhood graph, keeping the graph structure unchanged, disorganizing node features, and carrying out the data enhancement, thereby obtaining an enhanced graph; constructing an encoder based on a graph neural network, extracting spatial transcriptome data fused with spatial information and gene information to obtain potential embedding, and sending the potential embedding into a multi-space generator to generate multiple groups of rich graph feature representations; fusing graph feature representation and potential embedding to obtain refined representation, reconstructing a gene expression matrix through a decoder, and adding contrast learning loss and reconstruction loss as a total objective function; and updating network parameters by adopting an Adam optimizer according to the obtained total objective function to complete spatial transcriptome spatial domain identification. Spatial transcriptome data are fully mined from global and local angles, and accurate spatial domain identification is realized.

The invention discloses a laying hen genetic disease molecular marker screening system based on data fusion and AI prediction, the system comprises six modules, a multi-omics data acquisition module obtains laying hen genome and transcriptome data, and a FineDataLink data fusion module carries out feature alignment and association mapping to generate a fusion feature matrix; the dynamic time sequence diagram neural network processing module constructs a time sequence association diagram and outputs a time sequence feature vector, and the attention enhancement deep forest analysis module evaluates feature importance and outputs a screening result; the federal variation auto-encoder modeling module constructs a federal training framework to generate a molecular marker probability distribution model, and finally the molecular marker screening output module extracts key molecular markers. The system realizes deep fusion of multi-omics data and efficient application of an AI algorithm through multi-module cooperation, improves the molecular marker screening efficiency and accuracy, and provides technical support for disease-resistant breeding of laying hens.

The invention discloses a method for screening a Wilson disease serum exosome miRNA biomarker based on a transcriptomics technology, and belongs to the field of bioinformatics analysis of diseases. According to the invention, the effect of miRNA in WD pathogenesis is discussed by identifying serum exosome miRNA, and a potential biomarker is provided for accurate diagnosis and treatment of the disease. According to the invention, 59 DE-miRNAs are identified by analyzing the expression of differential miRNAs of WD and control group patients, and the DE-miRNAs are related to key approaches such as metabolic regulation, cancer progression, signal transduction and the like. Besides, the reliability of the key DE-miRNA, including miR-451a, miR-204-5p and the like, is confirmed through experimental verification, and the potential of the key DE-miRNA as a non-invasive biomarker is indicated. Research results provide important theoretical basis and new insight for accurate diagnosis of WD and development of potential therapeutic targets.

The invention provides a multi-slice spatial group data alignment and data enhancement method based on deep learning, which comprises the following steps: collecting slices of different tissues in a plurality of development stages, and obtaining gene expression data of a single cell level of each slice and spatial position information of each cell in the tissues by utilizing a spatial transcriptomics technology; based on the gene expression data and the spatial position information of each slice, constructing a spatial diagram by combining a K-nearest neighbor method and a circular neighborhood construction method, and mapping the spatial diagram to a shared embedding space by using a DGCNN network to obtain embedding distribution; respectively sampling the embedding representation of each cell point from the embedding distributions of the two slices, and outputting the probability of a real sample by using a discriminator of the generative adversarial network; spatial alignment of the two slices is achieved by minimizing the difference between the probability distributions of the two slices. According to the method, accurate spatial alignment is realized by integrating spatial information of different slices, and data enhancement is carried out by aggregating information of adjacent slices into a target slice.

The invention discloses a method for treating piglet damp-heat diarrhea through multi-omics combined analysis. According to the method, faeces metabonomics, intestinal transcriptomics and intestinal flora 16s analysis are adopted, transcriptome data, metabolome data and intestinal flora data are screened according to a prime difference standard, then transcriptomics of a model group and a control group, faeces metabonomics and intestinal flora are subjected to conjoint analysis, an intersection is taken, and the faeces metabonomics of the model group and the control group, the faeces metabonomics of the model group and the control group and the intestinal flora of the model group and the control group are subjected carrying out conjoint analysis on transcriptomics of the Model group and HQ-M, faeces metabonomics and intestinal flora, and then taking an intersection; the method comprises the following steps of: selecting a plurality of metabolites and florae from a plurality of intestinal florae, respectively obtaining intersections, screening related genes, searching gene FPKM expression quantity in transcriptomics, and screening corresponding metabolites and florae in metabonomics and intestinal florae to obtain a gene-flora-metabolite related network diagram. Multi-omics combined analysis shows that the piglet diarrhea due to damp-heat is mainly manifested by inflammation and glucose and lipid metabolism disorder.

The invention discloses a spatial transcriptome data analysis method based on artificial intelligence, and belongs to the technical field of spatial transcriptomics data analysis. Firstly, self-adaptive normalization and hypervariant gene screening preprocessing are carried out on original gene expression data; then constructing a hierarchical map integrating spatial proximity and transcription similarity, and ensuring the connectivity and robustness of the map through a dynamic radius pruning and neighborhood inheritance strategy; dividing positive and negative sample sets based on the atlas, and inputting a type modulation contrast graph auto-encoder for training; and finally, spatial domain identification and downstream function analysis are completed based on the low-dimensional potential representation or reconstructed gene expression matrix output by the model. The method effectively improves the accuracy and stability of spatial domain recognition, adapts to multi-technology-source data, enhances the biological interpretability of model output, and can be widely applied to biomedical scenes such as tumor microenvironment analysis and organ development research.

The invention provides a data integration method and device for a space transcriptome and a space metabolome, electronic equipment and a storage medium. The method comprises the steps that space transcriptome data points and space metabolome data points after space coordinate registration are obtained; determining a nearest neighbor space metabolome data point corresponding to each space transcriptome data point; fitting the original data at the nearest neighbor space metabolome data point by using the distance between the space transcriptome data point and the nearest neighbor space metabolome data point, and associating the original data with the space transcriptome data point; the reliability of a data integration result can be effectively improved, and the method can be compatible with scenes of different sequencing chip structures, so that diversified integration requirements are met.

Embodiments are directed to N3-kethoxal reagents and derivatives thereof, and related methods that allow fast and reversible labeling of single-stranded nucleic acids in live cells. By way of example, one aspect is directed to a process for reversible labeling of single-stranded guanine bases in live cells, which results in an effective in vivo method for transcriptome-wide RNA secondary structure mapping and RNA G-quadruplex prediction.

Systems and methods for processing spatial transcriptomic data and generating insights related to cell and tissue status. The systems and methods include spatial clustering, integration of a plurality of tissue sample data, and integration of scRNA-seq data with spatial transcriptomic data. The systems and methods combine graph self-supervised contrast learning and graph neural networks to perform spatial transcriptomics data processing.

The invention belongs to the technical field of biosynthesis of plant secondary metabolites, and particularly relates to a key gene for biosynthesis of large-fruit hawthorn flavonoid compounds and a screening method and application of the key gene. Through combined analysis of metabolome and transcriptome, a key gene combination containing seven structural genes and three transcription factor genes is screened out; the expression of the genes is remarkably positively correlated with the accumulation of a target flavone metabolite [6]-gingerol, and the genes are core factors for regulating and controlling the synthesis of the flavonoid compounds of the big-fruit hawthorns through experimental verification. According to the invention, the key gene for regulating and controlling the synthesis of flavone substances such as [6]-gingerol in the big hawthorn fruit is systematically identified for the first time, and a target spot is provided for analyzing a quality formation mechanism from a molecular level; the gene can be used for molecular marker-assisted breeding so as to cultivate a new variety of large-fruit hawthorn with high flavone content, and also can provide gene resources and technical support for the development of functional food and health care products.

The invention discloses a prognosis evaluation method and system for diffuse large B-cell lymphoma. According to the method, firstly, a gene module closely related to lipid metabolism is screened from DLBCL transcriptome data through weighted gene co-expression network analysis (WGCNA), and then eight key prognosis genes including FNDC1, IL22RA2, C15orf48, OMD, MFAP2, BC017398, CXCL6 and TNFAIP6 are identified from the module by adopting multi-step regression analysis (single factor Cox, LASSO and multi-factor Cox). A risk scoring model is constructed based on the expression levels and regression coefficients of the genes, and DLBCL patients can be divided into a high-risk group and a low-risk group with significant survival differences. The risk score and the clinical pathological factors are further integrated to construct a column graph, and individualized survival probability prediction can be achieved. The invention further provides a corresponding prognosis evaluation system, electronic equipment and a storage medium. An independent data set verifies that the prognosis model has excellent prediction performance and clinical practical value.

The invention belongs to the technical field of bioinformatics and medical data analysis, and discloses a neurodegenerative disease comprehensive analysis platform based on multi-source data fusion, and the platform comprises a data integration module; a gene name conversion module; a core analysis module; a network analysis module; a drug screening module; the omics visualization module systematically catalogs data of genes related to at least 10 main neurodegenerative diseases, 486 drug-derived 3, 957 bioactive components and 18 lifestyle factors through the data integration module, and the data source is wide; according to the method, artificially sorted literature evidence, reanalyzed batch transcriptome data, single-cell RNA sequencing data and standardized data from a public database are covered, new disease targets and potential treatment strategies can be found easily, and the neurodegenerative disease research efficiency and comprehensiveness are improved.

The invention belongs to the field of biotechnology and nutritional medicine, relates to a maternal obesity intervention preparation, and particularly relates to a preparation method of a plant embryo fermentation product and application of the plant embryo fermentation product in blocking of trans-generation delivery metabolic diseases. FWG is prepared through a probiotic fermentation technology, obesity, glucose and lipid metabolism disorder and oxidative stress induced by high-fat diet of a parent body can be remarkably improved, and cross-generation transmission of metabolic diseases is blocked by adjusting composition of intestinal flora of the parent body and offspring and a hypothalamic PI3K-Akt signal channel. The invention reveals that maternal FWG intervention passes through a mechanism that a PI3K-Akt pathway is dominated and multiple pathways are coordinated on the transcriptome level for the first time, the HFD-induced offspring hypothalamus metabolism programming abnormality is reversed, and a new central regulation target is provided for cross-generation metabolic disease prevention. The FWG is a wheat processing byproduct, is free of chemical additives, is suitable for long-term dietary supplement, is a natural and safe nutrition intervention scheme, and is suitable for the field of maternal and infant health.

The invention provides a method for jointly deducing dynamic cell communication and a cell state transition rate. Relates to the technical field of biological information. The method comprises the following steps: extracting candidate ligands, candidate receptors and characteristic genes from space transcriptome data to be processed; the method comprises the following steps: screening nodes which have an interaction relationship with candidate ligands, candidate receptors and characteristic genes from a pre-constructed prior database, and constructing a multi-layer signal network of which the structure is ligand-receptor-transcription factor-target genes; establishing a gene regulation kinetic model according to a ligand-receptor action relationship, a receptor-transcription factor action relationship and a transcription factor-target gene action relationship in the multilayer signal network; iteratively optimizing parameters to be estimated in the gene regulation and control kinetic model; and obtaining the change rate of the expression quantity of the target gene in the receiving cell based on the potential time after iterative optimization and the parameters to be estimated after iterative optimization. And combined analysis of dynamic cell communication and cell differentiation tracks can be realized.

The invention provides a method, device and system for evaluating the effect of intervening small molecules by tumors and a storage medium, and relates to the technical field of bioinformatics, the method comprises the following steps: obtaining a first transcriptome representation of a tumor cell population before target small molecule intervention and a second transcriptome representation of a normal tissue cell population corresponding to the tumors; inputting the first transcriptome representation and the condition vector into a prediction model to obtain a third transcriptome representation; calculating a first distance and a second distance; an intervention effect is evaluated based on a comparison of the first distance and the second distance. According to the method, the influence of the drug on the transcriptome of the whole cell population is efficiently predicted, and the distance between the transcriptome and the health state is quantitatively compared, so that a set of brand new standard for improving the evaluation target from the traditional'killing cell 'to'reversing disease state' is established, and an objective decision basis is provided for developing more accurate drugs.

The invention discloses an NDRV [sigma] C circular RNA vaccine and application thereof, and belongs to the technical field of vaccines.Duck hepatitis A virus type 1 (DHAV-1) UTR and T4 bacteriophage I type intron self-splicing cyclization systems are adopted to construct circular RNA (CirRNA-[sigma] C) for expressing NDRV [sigma] C protein, and a vaccine preparation is prepared through a chitosan-polyethyleneimine (CS-PEI) nano delivery system. The feasibility of a circular RNA vaccine system based on DHAV-1UTR in poultry vaccines is confirmed for the first time, the established nano delivery technology and circular RNA vaccine platform have the characteristics of high stability, good safety, strong immunogenicity and the like, and an important technical path is provided for development of novel vaccines of NDRV and other viral epidemic diseases.

The invention discloses a space transcriptome and space metabolome integration method based on deep learning. The method comprises the following steps: firstly, acquiring original space transcriptomics data and original space metabonomics data of a biological tissue, and performing data preprocessing to obtain a preprocessed biological tissue data set; then, aligning space metabonomics data in the preprocessed biological tissue data set to space transcriptomics data, unifying data resolution, obtaining corrected space metabonomics data, and updating the biological tissue data set; and finally, generating to-be-integrated sample data from the newest biological tissue data set, inputting the to-be-integrated sample data into the spatial multi-omics data integration model, and outputting final joint embedding by the model, so that cross-modal and cross-sample effective integration of the data can be realized. According to the method, the problems of form and resolution inconsistency and batch effect caused by technical difference of ST and SM data are solved, and high-precision and interpretable spatial multi-omics integration analysis is realized.

The present invention generally relates to systems and methods for imaging or determining nucleic acids, for instance, within cells. In some embodiments, the transcriptome of a cell may be determined. Certain embodiments are directed to determining nucleic acids, such as mRNA, within cells at relatively high resolutions. In some embodiments, a plurality of nucleic acid probes may be applied to a sample, and their binding within the sample determined, e.g., using fluorescence, to determine locations of the nucleic acid probes within the sample. In some embodiments, codewords may be based on the binding of the plurality of nucleic acid probes, and in some cases, the codewords may define an error-correcting code to reduce or prevent misidentification of the nucleic acids. In certain cases, a relatively large number of different targets may be identified using a relatively small number of labels, e.g., by using various combinatorial approaches.

The invention relates to the technical field of bioinformatics, in particular to an esophageal cancer RNA-protein interaction space prediction method and device based on a graph neural network, and can solve the problem that it is difficult to accurately distinguish the difference of cancer tissue and normal tissue on RPI in a traditional method to a certain extent. The method comprises the steps of obtaining multi-stage space transcriptome data related to esophageal cancer, and performing preprocessing to obtain a multi-modal feature tensor and a canceration stage tag; using the multi-modal feature tensor and the canceration stage tag, using RNA and protein as nodes, defining a static edge based on sequence complementarity and structure matching degree, defining a dynamic edge based on spatial co-expression correlation, and constructing a space-time dynamic graph updated along with the canceration stage; the space-time dynamic graph serves as input, interaction existence, site coordinates and binding energy are predicted through space-time double-branch coding and attention mechanism fusion features, and an interaction prediction result is output.

Systems and methods for multi-modal subcellular segmentation using photolysable biomarkers and / or transcriptomic readout density maps are disclosed. The systems and methods improve the accuracy of cell segmentation of the nucleus, cytoplasm, and cell membrane regions by using optical and bleach correction from a variety of photolysable morphological markers in combination with high quality 3D images acquired with high dynamic range scans and spatial transcriptomic readout density maps.