1374 results about "Transcriptome" patented technology

Provided herein are methods of identifying genetic variants in biological samples. In particular, the present disclosure uses spatial transcriptomic templated ligation methods and compositions to identify the presence or absence, and the location of genetic variants in the transcriptome or genome of a biological sample.

The invention discloses a space transcriptomics cell type deconvolution method based on a generative adversarial network and a comparative learning framework. The method comprises the following steps: firstly, enhancing a gene expression matrix by using a histological image and spatial position information; then, in combination with GraphSAGE and self-supervised comparative learning, optimizing feature representation of space transcriptomics data, and reconstructing high-quality space gene expression data; single-cell RNA sequencing data gene expression features are extracted by using a variational automatic encoder, and meanwhile, the quality of a generated sample is optimized by using a discriminator, so that efficient feature extraction and accurate gene expression reconstruction are realized; and finally, mapping single-cell RNA sequencing data into spatial points through comparative learning and mapping matrix learning based on low-rank representation, thereby predicting the cell type of each point. According to the model, multi-modal information in single-cell RNA sequencing and space transcriptomics data is comprehensively considered, and a mapping matrix is constructed and optimized, so that the cell type of each space point is predicted.

The invention discloses a graph convolution spatial domain identification method of a fusion gene interaction network, and relates to the technical field of biological information, and the method comprises the steps: preprocessing spatial transcriptome data; constructing a spatial diagram according to the spatial position information of the spot and the gene expression matrix; constructing a conditional gene interaction network based on at least a part of key genes in the spatial transcriptome data, constructing a conditional gene interaction network matrix, combining the conditional gene interaction network matrix and the gene expression matrix to obtain an expression degree-interaction matrix, and constructing a feature map; performing graph neural network convolution on the space graph and the feature graph, and introducing an attention mechanism to obtain final embedding; and clustering and finally embedding, and identifying a spatial domain with a similar gene expression mode. According to the method, the spatial domain is identified through the fusion gene interaction network, the problem that the spatial domain is discontinuous is effectively solved, the robustness and accuracy of the spatial domain identification method are improved, and high performance is shown in complex and highly heterogeneous tissue samples.

The invention belongs to the technical field of biology, and discloses a reagent combination or kit for constructing intestinal organs and application of the reagent combination or kit. According to the reagent combination or the kit, intestinal organs can be obtained from cell-derived epithelial cells obtained from a donor in a non-invasive manner, and the obtained intestinal organs can be cryopreserved and recovered and can be amplified in vitro for a long time; transcriptome characteristics are similar to those of real human small intestine tissues, and typical marker genes of various small intestine pedigree cell types are highly expressed; compared with intestinal organs obtained through induction of pluripotent stem cells, the intestinal organs have more intestinal pedigree characteristics, and the intestinal function development is more mature; after being promoted to be mature, the intestinal organ also highly expresses genes related to drug absorption and metabolism, has drug absorption capability similar to that of an immortalized intestinal cell line, but more prominently shows intestinal cell lineage characteristics, is closer to an intestinal environment in a real human body, and can be used for screening intestinal disease drugs; the intestinal barrier function is realized.

The invention discloses an interpretable machine learning genome prediction method and device, belongs to the technical field of combination of biological breeding, biological information and machine learning, and utilizes an advanced machine learning algorithm to perform parameter optimization in combination with biological prior information. Through processing of multi-source data (genome, transcriptome and epigenetic data), dynamic feature engineering (PCA and PHATE dimensionality reduction) and organic combination of various machine learning models, and an automatic parameter adjustment framework based on a grid search and sparrow search algorithm, genome prediction precision and calculation efficiency are significantly improved; meanwhile, the interpretability of the model is realized based on the SHAP value, the SNP site contribution is quantified, a reference is provided for precise breeding, and the method is suitable for animal and plant molecular breeding and medical genetic analysis, and can accelerate genetic analysis of high-value characters, assist precise breeding decision and disease risk prediction, and promote leap-forward development from experience breeding to intelligent breeding.

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 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 discloses a spatial domain identification method based on a multi-view weighted fusion GCN network, and the method comprises the steps: taking the gene expression and spatial position of spatial transcriptome data as input, and combining the spatial information based on different similarity measurement modeling with the gene expression after modeling to construct a multi-view weighted fusion graph convolutional network model; and reconstructing a gene expression matrix by using a decoder, capturing global information of ST data, calculating spatial regularization constraint loss by using similarity information and spatial neighbor information, and completing identification of a spatial transcriptome spatial domain. According to the method provided by the scheme, the model can extract the spatial information from the whole situation and the local situation, so that the spatial information is more fully and comprehensively mined, and accurate spatial domain identification is realized.

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.

Described herein are systems and methods of multi-modal subcellular segmentation using photo-cleavable biomarkers and / or transcriptomic readout density maps. The systems and methods provide an improvement in cell segmentation accuracy for nuclei, cytoplasm and membrane regions through the use of optical and bleaching corrections from multiple sources of photo-cleavable morphology markers in combination with high-quality 3D images acquired with high-dynamic range scanning, and spatial transcriptomic readout density maps.

The invention relates to the technical field of spatial transcriptome data clustering analysis, and discloses a spatial transcriptome data clustering method based on progressive learning and multi-modal fusion. According to the method, a high-order structure relationship among sample points is captured by using a multilayer graph convolutional network, and various noises of gene expression data are removed to the greatest extent through comparative learning and a complementary mask mechanism; meanwhile, gene expression data, spatial position information and histological image information are ingeniously fused to the maximum degree through two times of cross attention, and then fusion feature latent representation Latent is obtained. Compared with a mainstream spatial transcriptomics clustering analysis method, the method has the advantages that clustering based on the fusion feature latent representation Latent shows higher capability and excellent clustering performance, and has remarkable robustness.

The invention discloses an application of RhALEU protein and a coding gene thereof in senescence regulation and control of Chinese rose petals. According to the invention, the RhALEU gene is screened from a Chinese rose transcriptome database, and research finds that the expression of RhALEU is induced in the whole petal development stage. A VIGS technology is utilized to verify that the RhALEU gene has a function of promoting the aging of Chinese rose petals. The silence of the RhALEU gene slows down the speed of petals from level 5 to loss of ornamental value. In petals with the silent RhALEU gene, the expression of the senescence-related gene SAG12 is remarkably reduced. Research results of the invention reveal the effect of the RhALEU gene in natural aging of petals, a new thought and technical means are provided for regulating and controlling plant flower aging, especially post-harvest preservation of fresh Chinese rose flowers, a new flower regulation and control research field is developed, and the RhALEU gene has great academic value and important production and application prospects.

The invention provides a method for identifying cell communication based on multi-omics data. The method is realized by the following steps: step 1, acquiring a single cell and space transcriptome data set; 2, preprocessing the single cell and the spatial transcriptomics data set; and step 3, constructing a deep neural network model. And step 4, training the constructed deep neural network model by using the preprocessed single cell and the space transcriptomics data set. And 5, carrying out intercellular communication identification on to-be-detected data by using the trained deep neural network model. According to the method, an excellent inter-cell communication recognition effect can be achieved on multiple sets of single-cell and spatial transcriptomics data sets. Compared with other latest intercellular communication identification methods, the method shows more excellent performance on a plurality of evaluation indexes. In addition, under the condition of different degrees of noise and on a missing space transcriptome data set, high stability and accuracy are achieved.

This disclosure describes a Machine Learning (ML) framework, including graph convolution neural networks (GCN) for identifying gene expression signatures related to cancer driver events. The model is trained to identify the TP53 mutational state of cancer samples from gene expression, utilizing comprehensive curated graph structures of gene interactions. A quantitative score is generated to rank the severity of a driver event in each sample. Very high AUG results on unseen data across several tumor types are achieved with this method. There is a strong correlation with protein function. The Signatures in Transcriptome Associated with Mutated Protein (STAMP) model can also predict driver events in many combinations of important cancer genes / pathways and several tumor types, based on well-established annotations from the literature. The STAMP model therefore can identify and quantify driver events, which may lead the way to improved targeted therapy selection and prioritization in cancer patients.

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 belongs to the technical field of biological medicines, and discloses an angiotensin I converting enzyme inhibitory peptide. According to the present invention, the action mechanisms of two ACE inhibitory peptides H5 (LIIPQH, 719.85 Da) and H6 (LIIPEH, 720.85 Da) extracted, separated and purified from rice wine residue are researched, and the in vitro activity of the ACE inhibitory peptides is researched through an EA.hy926 cell model, such that the two ACE inhibitory peptides can provide the activity effect by promoting the release of NO, the expression of eNOS and SOD, and inhibiting the release secretion of ET-1 and the release of ROS; meanwhile, the cells are subjected to transcriptomics analysis, and the pathway mechanism of polypeptide action is defined. The ACE inhibitory peptide is expected to serve as a potential antihypertensive drug or a functional food ingredient, the high-value utilization of the rice wine residues is improved, and a new application way is provided for the food-borne ACE inhibitory peptide in the aspect of treating hypertension.

The disclosed technology relates to methods, transcriptomics systems, and non-transitory computer readable media for enabling natural language transcriptomics analysis. In some examples, genomic data including gene expression profiles for cells is transformed into sequences of genes ordered by expression level for each of the cells. The sequences of genes are annotated with metadata in a natural language format. A large language model (LLM) is then fine-tuned using the annotated sequences. The LLM is pretrained for natural language processing (NLP) tasks. The fine-tuned LLM is applied to generate and output a result in response to a received prompt in the natural language format. Thus, the LLMs of this technology advantageously both generate and interpret transcriptomics data and interact in natural language to generate meaningful text from cells and valid genes, among many other types of results.

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

The 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.

A method for inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology includes receiving at least one histology image of a tissue sample at a histology feature extractor. The histology feature extractor partitions the at least one histology image into image tiles and partitions each of the image tiles into image sub-tiles. The histology feature extractor extracts histology features from the histology image, the extracted histology features include low-level image features extracted from the image sub-tiles and high-level image features extracted from the image tiles. A super-resolution gene expression predictor predicts gene expression for the image sub-tiles using the extracted histology features and a predictor model trained with spot-level gene expression observations. A tissue architecture annotator clusters the image sub-tiles based on the predicted gene expression of the image sub-tiles and annotates each of the image sub-tiles using the predicted gene expressions and a marker gene reference panel.

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 belongs to the technical field of biomedicine and genes, and particularly relates to an application of a transcriptomics-based machine learning model in chronic kidney disease renal injury gene identification, and the construction of the transcriptomics-based machine learning model comprises the following steps: S1, training gene identification by machine learning; s3, performing feature screening; s4, feature gene verification; s5, feature gene causal inference; s6, a correlation analysis result of the expression quantity of the characteristic gene and GFR and SCr shows kidney injury diagnosis and monitoring value. The column graph model based on feature gene expression, which is constructed by the invention, overcomes the prejudice in the prior art, integrates the feature genes for scoring, has relatively high accuracy, and provides a theoretical basis for kidney injury risk prediction, auxiliary clinical early diagnosis and illness state assessment.

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 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 construction method of a microplastic toxicity prediction model based on transcriptomics and a QSAR (Quantitative Synthetic Aperture Radar) model, which comprises the following steps: measuring actual observation values of cell survival rates of different microplastic samples after contamination and changes of genes in the cells, and screening out the genes as specific genes; selecting a physicochemical descriptor and then determining a gene expression descriptor of the specific gene; establishing a training set and a test set; a plurality of machine learning algorithms are utilized to construct QSAR models respectively, and an optimal QSAR model is selected according to a performance evaluation result; and carrying out performance verification on the optimal QSAR model, and screening out a microplastic toxicity prediction range suitable for the optimal QSAR model. The construction method of the microplastic toxicity prediction model can realize reliable prediction of toxicity, and can effectively support safety evaluation and biotoxicity mechanism research of microplastics.

The invention discloses a method for screening and identifying a cucumber aphid-resistant gene as well as a screened gene and application thereof, and provides a brand-new method for screening and identifying the cucumber aphid-resistant gene, which is used for screening the cucumber aphid-resistant gene based on four omics technologies. The four types of omics comprise transcriptome, proteome, methylation group and metabolome before and after inoculation of aphids on cucumbers, and the cucumber aphid resistance regulation gene is screened through combined analysis, so that a new gene resource is provided for improving the aphid resistance of the cucumbers, and a new method and a new idea are provided for designing molecular genetic breeding. The CsPGD2 gene obtained through screening is an aphid resistance gene, the dynamic change of a metabolic substance 5-ribulose phosphate in a metabolome is consistent with that of a transcriptome gene CsPGD2, the aphid resistance function of the gene CsPGD2 is verified through VIGS, and the result proves that the gene CsPGD2 is an important regulation factor for the aphid resistance of cucumbers. The invention provides a new gene resource for improving the aphid resistance of cucumbers, and has a good application prospect.

The invention discloses a space transcriptome data analysis method based on edge weighted graph attention and multi-view comparative learning, which comprises the following steps: preprocessing space transcriptome data to obtain preprocessed space transcriptome data; wherein the spatial transcriptome data comprises histological information, gene expression information and spatial position information; constructing a cell adjacency matrix, a disturbance matrix and a gene co-expression matrix based on the preprocessed space transcriptome data; based on the cell adjacency matrix, the perturbation matrix and the gene co-expression matrix, a multi-view comparison learning framework is constructed, and multiple views comprise a front view, a space view and a negative view; the multiple views are mapped to a unified potential representation space through an edge weighted graph attention auto-encoder, and node embedding is obtained; downstream analysis is performed based on node embedding. According to the method, different space structure modes can be accurately identified in space transcriptome data analysis, so that the clustering accuracy, compactness and separability of space data are improved.

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.