169 results about "Gene expression profiling" patented technology

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 present disclosure generally relates to gene expression profiling of tissue samples obtained from colorectal cancer patients who are candidates for chemotherapy treatment. More specifically, the disclosure provides methods based on characterization of gene expression which allow a physician to predict whether a patient is likely to respond well to treatment with a chemotherapeutic reagent.

The invention relates to the technical field of medical informatics, and particularly discloses a construction method of a breast cancer lung metastasis prognosis prediction model. Comprising the following steps: (1) acquiring breast cancer in-situ and lung metastasis single cell RNA sequencing data by a GEO database, and preliminarily screening differential genes; (2) acquiring clinical information and gene expression profile information data of the breast cancer patient by a TCGA database, performing single-factor COX regression analysis, and screening genes related to the total survival rate; and (3) screening genes related to the total survival rate through LASSO regression to obtain six model genes, and calculating a risk scoring formula according to the expression quantity of the six model genes to complete the construction of the breast cancer pulmonary metastasis prognosis prediction model. According to the invention, scRNA-seq technical data from GSE202695 and a TCGA database are combined to develop a breast cancer lung metastasis prognosis prediction model based on six model gene characteristics of breast cancer lung metastasis.

The invention relates to the field of intelligent medical treatment, in particular to a new antigen screening method, equipment and a program product. Comprising the following steps: S1, acquiring tumor slice data and serum data of a patient; s2, performing data processing on the tumor slice data and the serum data to obtain an HLA / MHC pseudo sequence, antigen peptide data and an antigen gene expression level; the data processing comprises data sequencing, HLA / MHC typing, somatic mutation calculation, fusion gene calculation and gene expression profile calculation; s3, screening new antigens based on the HLA / MHC pseudo sequence, the antigen peptide data and the antigen gene expression level data to obtain the new antigens, the application can perform new antigen screening, and has good clinical value.

By a genome-wide gene analysis of expression profiles of over 50,000 known or putative gene sequences in peripheral blood, the present inventors have identified a consensus set of gene expression-based molecular biomarkers associated with chronic allograft nephropathy and / or interstitial fibrosis and tubular atrophy CAN / IFTA and subtypes thereof. These genes sets are useful for diagnosis, prognosis, monitoring and / or subtyping of CAN / IFTA.

The invention discloses the technical field of pathology and space transcriptomics, and particularly relates to a gene expression prediction method and system based on multi-modal comparative learning and a guidance mechanism. Cutting the histological slice image into image blocks according to space coordinates; according to the method, a local convolution branch and a global Transform branch are combined to extract image features, the image features are mapped to a shared potential space through projection, soft contrast, hard contrast and global consistency constraints are introduced into the space, and cross-modal alignment of an image modal and a gene expression modal is realized; an expression prediction head is introduced in the training stage, representation learning is directly guided by a regression signal, and the relation between feature learning and gene expression prediction is broken through; in the inference stage, k-nearest neighbor retrieval and a multi-distance weighted aggregation strategy are combined to infer a gene expression profile of an unknown position. According to the method, the accuracy and robustness of space gene expression prediction can be effectively improved, the tissue space heterogeneity structure is kept, and the method has high clinical application and scientific research and popularization value.

The invention discloses a compound perturbed cell line gene transcription profile prediction method, device and equipment and a medium, and relates to the field of compound perturbed gene transcription profile prediction. According to the method, a combined loss function including a mean square error, Pearson's correlation coefficient loss and KL divergence loss is firstly constructed, then a deep neural network model is trained based on the combined loss function, a gene transcription profile prediction model is obtained, prediction of gene expression profile information after compound disturbance is achieved, and the prediction accuracy of the gene expression profile information is improved. The optimization strategy based on the mean square error, the Pearson correlation loss and the KL divergence loss is excellent in numerical accuracy and trend consistency, the method can accurately predict the expression level after gene disturbance, it can be ensured that the gene expression change mode is highly related to the real biological change, and the method has good application prospects. Therefore, the generalization ability under disturbance scenes of different cell lines and compounds is improved.

The embodiment of the invention provides a gene data processing method and system, electronic equipment and a storage medium, and belongs to the technical field of gene analysis. The method comprises the following steps: obtaining genotype data of a to-be-detected individual, and determining a plurality of associated genes of the to-be-detected individual according to the genotype data and single nucleotide polymorphism data associated with a target risk task; querying associated gene features corresponding to each associated gene in a pre-constructed gene feature library, wherein the gene feature library comprises embedding representations of different genes constructed based on an embedding model; the embedded model is obtained by performing self-supervised learning on a plurality of gene expression profiles of the same species as the individual to be detected; carrying out feature integration on the plurality of associated gene features to obtain individual features; and calling a prediction model to process the individual features to obtain a risk score of the to-be-detected individual corresponding to the target risk task. The method can improve the accuracy of gene data processing.

The invention discloses a method and a device for processing single cell gene expression profile data of different platforms, electronic equipment and a storage medium, which are used for converting the single cell gene expression profile data of different platforms into a gene expression matrix file of a single sample, a first column in the gene expression matrix file is a gene name, reading the gene expression matrix file of a single sample, and converting the gene expression matrix file of the single sample into a single sample data object; performing principal component analysis on all the single-sample data objects to obtain a preset number of principal components of a gene expression profile in each single-sample data object; correcting a preset number of principal components of the gene expression profile by taking samples of different platforms and single cell experimental methods as covariables to obtain a corrected first principal component and a corrected second principal component; and drawing a double plot of the corrected first principal component and the second principal component.

A system may receive a plurality of images that capture at least one cell having a complex shape, the plurality of images comprising first and second stained images. A system may receive a data matrix that represents a gene expression profile of the at least one cell. A system may combine the first and second stained images to create an overlay image. A system may analyze at least one of the second stained image or the overlay image to delineate a boundary of the at least one cell. A system may create a mask based on the boundary of the at least one cell in the at least one of the second stained image or the overlay image. A system may combine the mask and the data matrix to form a combined image for the at least one cell.

The invention provides a hypergraph-based weighted gene co-expression hypernetwork module identification method and system, and the method comprises the steps: obtaining a gene expression profile matrix, and integrating an effective gene set of each sample to construct a weighted hypergraph; calculating a Laplacian matrix of the weighted hypergraph, and obtaining a topological overlapping matrix based on the Laplacian matrix; and based on the topological overlapping matrix, performing module identification on the gene by adopting a hierarchical clustering method, and outputting a gene co-expression module. According to the method, the calculation efficiency is effectively improved by optimizing hypergraph construction and utilizing the hypergraph Laplacian matrix to carry out network analysis. Compared with a traditional WGCNA method, the WGCHNA shows higher calculation speed and processing capacity when processing a large-scale gene data set, so that the gene co-expression network can be analyzed more quickly, and the requirements of multiple samples and complex networks are met.

Lymphedema is characterized by the accumulation of protein-rich interstitial fluid, lipids and a significant inflammatory cell infiltrate in the limb. It causes a significant morbidity and is a common disabling disease affecting more than 250 million people worldwide, however there is no curative treatment for lymphedema. Here, the inventors found that dermolipectomies from patient with lymphedema exhibit inflamed gene expression profile compared to normal arm on same patient. After lipidomic analysis, the inventors identified severe decrease in arachidonic acid-derived lipid mediators generated by the 15-lipoxygenase (15-LOX) in lymphedematous arms. Using a mouse model of lymphedema, they reproduced the etiology of the human pathology including the loss of specialized pro-resolving lipid mediators that play essential roles in resolution of inflammation. This was associated with a lack of regulatory T cells (Treg) recruitment in the injured limb adipose tissue. Importantly, the inventors identified the lymphatic endothelial 15-LOX was responsible for the chemoattraction and transendothelial migration of Tregs. These results were confirmed by an aggravation of lymphedema and deterioration of the lymphatic network in an original transgenic mouse model in which ALOX15 gene is selectively deleted in the lymphatic system. Importantly, this was reversed by the injection of 15-LOX expressing lentivectors. These results provide evidence that lymphatic lipoxygenase may represent a novel therapeutic target for lymphedema by serving as a mediator of Treg invasion into lymphedematous adipose tissue.

By a genome-wide gene analysis of expression profiles of over 50,000 known or putative gene sequences in peripheral blood, the present inventors have identified a consensus set of gene expression-based molecular biomarkers associated with chronic allograft nephropathy and / or interstitial fibrosis and tubular atrophy CAN / IFTA and subtypes thereof. These genes sets are useful for diagnosis, prognosis, monitoring and / or subtyping of CAN / IFTA.

The invention relates to a method of predicting an outcome of a subject having a glioma, comprising determining or receiving the result of a determination of a first gene expression profile for each of one or more immune defense response genes, and / or of a second gene expression profile for each of one or more T-Cell receptor signaling genes, and / or of a third gene expression profile for each of one or more PDE4D7 correlated genes, said first, second, and third expression profile(s) being determined in a biological sample obtained from the subject, determining the prediction of outcome based on the first gene expression profile(s), or on the second gene expression profile(s), or on the third gene expression profile(s), or on the first, second, and third gene expression profile(s), and, optionally, providing the prediction to a medical caregiver or the subject.

The invention relates to the technical field of jerusalem artichoke breeding and planting, and discloses a jerusalem artichoke variety breeding and planting method and system. The method comprises the following steps: collecting phenotype data of jerusalem artichoke plants at different growth stages Constructing a multi-dimensional growth model containing an incidence relation between environmental factors and phenotypic data, wherein the environmental factors are obtained by monitoring a planting area sensor array in real time; screening candidate jerusalem artichoke strains in the target environment parameter interval based on the phenotype-environment response curve; hybridizing and matching the candidate strains to obtain filial generation seeds, and sowing the filial generation seeds in a controllable environment test field; extracting gene expression profile data of offspring seeds, and establishing a genotype-phenotype incidence matrix in combination with the response curve; transplanting the dominant group to a field, and collecting growth dynamic data to update the multi-dimensional growth model; an environment control strategy is adjusted according to the updated model, and dominant groups are directionally cultivated; tubers are harvested and subjected to quality detection, and the result is fed back to the incidence matrix to optimize the next breeding process.

The invention discloses an scRNA-seq data feature learning method based on a graph automatic encoder, and belongs to the technical field of biological information. According to the scRNA-seq data self-encoder model, the problems that a traditional self-encoder model neglects the incidence relation between cells in the scRNA-seq data and effective feature representation of the scRNA-seq data is difficult to capture are solved. The scRNAseq data feature learning method which can process a large-scale data set and has high expandability is provided from the perspective of a graph automatic encoder, so that features are learned again according to a gene expression profile of a single cell and an expression matrix is reconstructed; according to the method, the incidence relation between cells in the scRNA-seq data and the effective feature representation of the scRNA-seq data can be captured, the obtained matrix can be subjected to biological analysis such as dimension reduction and clustering, better clustering precision and more accurate and richer biological information can be obtained, and a better downstream analysis effect can be obtained. The method disclosed by the invention can be used for acquiring scRNA-seq data characteristics.

The present invention belongs to the field of biomedicine, and specifically relates to a prognostic model for predicting the overall survival rate of AML patients based on 9 fatty acid metabolism-related genes. The present invention analyzed the gene expression profiles of 354 AML patients from TCGA and VIZOME, determined the prognosis-related genes based on the fatty acid metabolism-related genes of the TCGA dataset using univariate Cox regression analysis and survival analysis, and established a prognostic model with 9 fatty acid-related genes. This model can evaluate the prognosis of AML patients more specifically and sensitively. Then, using a nomogram, the fatty acid metabolism gene prognostic model, age, and cytogenetic risk degree were integrated into a scoring system to more accurately predict the survival of AML patients, with broad clinical application prospects.

The invention relates to a library construction method for UMI and Poly-A tail analysis and application of the library construction method. According to the invention, a novel library construction process is designed, a UMI + Poly-A tail tandem library process is established, all sequences of the Poly-A tail can be completely obtained, the method can be used for simultaneous sequencing analysis of UMI and Poly-A tail, absolute quantification can be carried out on original transcripts, the sequencing efficiency and precision are improved, short PCR products are connected in series, long fragments are formed, and the sequencing time is shortened. The advantages of long sequencing length and long reading length of Pacbio can be fully utilized, the most original RNA molecules are accurately backtracked and counted, and a real gene expression profile is obtained.

The invention relates to a drug combination data processing method and system based on organ adverse reactions, and the method comprises the steps: carrying out the graph neural network fusion of multi-source patient medical data, and carrying out the cross-scale integration of organ function parameters, metabolic characteristics and hemodynamic data; extracting a core biomarker by utilizing principal component analysis and organ specificity screening, and generating a feature vector representing organ reaction; constructing a nonlinear drug interaction model by combining a dose-time sequence and in-vitro experimental data, and quantifying the strength of synergy and antagonism; gene expression profiles, pathological states and lifestyle data are introduced for triple dynamic calibration, and individualized adverse reaction probability optimization is achieved; monte Carlo sampling is adopted to simulate an organ toxicity scene, and intelligent classification decision is performed on a high-risk combination scheme based on a supervision rule base. The method breaks through the limitation of data islands, static evaluation and individualization deficiency in the traditional technology, and significantly improves the prediction precision and clinical applicability of the drug combination safety.

The invention relates to the technical field of deep learning, in particular to an application of a deep learning technology in drug research and development, and particularly relates to a virtual screening algorithm based on a gene expression profile and comparative learning, which comprises the following steps: on the basis of comparative learning, defining a drug and a matched expression profile as positive samples and other pairs in batches as negative samples; a double-feature encoder is adopted, and the cosine similarity is used as a scoring function to carry out similarity measurement. And the two loss function training models are combined, so that the performance of virtual drug screening is improved. In a word, the method marks an important step of virtual drug screening.

The present invention belongs to the field of gene expression data analysis, and particularly relates to a method for constructing a graph neural network data set based on gene expression; it includes obtaining gene expression profile data of a disease and extracting RNA data, processing the RNA data to generate an RNA expression matrix; performing standardization processing and differential analysis on the generated RNA expression matrix to obtain a differential expression matrix; using WGCNA to analyze the differential expression matrix and construct an overlapping topological matrix; exporting all edge information and node information in the overlapping topological matrix, and encoding the exported information; screening the encoded information, using the screened information to construct a graph neural network data set, and identifying biomarkers according to the graph neural network data set; the present invention performs WGCNA analysis on differentially expressed genes, pays attention to the relationship between genes, and constructs a graph neural network data set by screening node and edge information, which is beneficial to discovering biomarkers of tumors.

The present invention discloses a predictive marker for novel coronavirus susceptibility and a prediction method and device, which are used to solve the problems of unstable biomarker discovery, large risk prediction limitations and low feasibility in existing related methods. The method includes: first, providing a predictive marker for novel coronavirus susceptibility, wherein the predictive marker is a differentially expressed gene set extracted based on a human blood sample; second, providing a corresponding method for predicting novel coronavirus susceptibility, obtaining a blood sample to be tested, and extracting a PBMC sample to be tested from the blood sample to be tested; extracting the gene expression data of the predictive marker in the PBMC sample to be tested from the gene expression spectrum; inputting the gene expression data to be tested into a pre-trained novel coronavirus susceptibility prediction model for susceptibility risk judgment, and obtaining the novel coronavirus susceptibility risk assessment result of the human body corresponding to the blood sample to be tested, thereby simply, quickly and accurately predicting the human body's novel coronavirus susceptibility risk.

By a genome-wide gene analysis of expression profiles of over 50,000 known or putative gene sequences in peripheral blood, the present inventors have identified a consensus set of gene expression-based molecular biomarkers associated with subclinical acute rejection (subAR). These genes sets are useful for diagnosis, prognosis, monitoring of subAR.

The invention relates to the field of disease diagnosis, and particularly discloses a marker for olfactory neuroblastoma subtype classification, and the marker comprises a substrate type marker, a nerve type marker and a mesenchymal type marker. The typing gene expression profile used in the invention is derived from high-precision single cell sequencing, has high accuracy and distinction degree, and can be projected to ordinary transcriptome sequencing with relatively simple sequencing steps and low cost to obtain a typing conclusion, so that verification and popularization within a certain range are facilitated, and the method has a wide application prospect. The kit is helpful for typing ONB patients in clinical work, and assists corresponding drug treatment selection according to molecular characteristics of each subtype.

The invention provides a space transcriptome data analysis system based on a PDX model, and relates to the technical field of biological information analysis. Comprising the following steps: extracting a coordinate identifier, a molecular identifier and a sample transcript sequence in a sequencing sequence; analyzing a sample transcript sequence, and preliminarily classifying a sequencing sequence into a first sequence, a second sequence, an uncertain sequence and a discarded sequence; calculating a first ratio under each sub-box; classifying the sub-boxes into a first sub-box and a second sub-box according to the first ratio; re-classifying the uncertain sequences in the first sub-box into a first sequence, and re-classifying the uncertain sequences in the second sub-box into a second sequence; and S104, respectively merging and analyzing all the first sequences and the second sequences to obtain a first space gene expression profile and a second space gene expression profile. According to the method provided by the invention, the distribution accuracy of the gene expression quantity of the space transcriptome and the utilization rate of the sequencing sequence are improved.

Methods, systems and apparatus are described for generating synthetic gene expression profile datasets for downstream biological / pharmacological analysis, processing and / or applications Generating synthetic gene expression profile datasets includes: receiving a gene expression profile dataset comprising data representative of at least one gene expression profile; computing a complex gene expression profile dataset based on applying a discrete Fourier transform to data representative of each gene expression profile of the received gene expression profile dataset; sampling the complex gene expression profile dataset based on using a statistical distribution to sample and modify a predetermined number of one or more components of each complex gene expression profile for generating a plurality of synthetic complex gene expression profiles; computing a real-valued synthetic gene expression profile dataset based on applying an inverse discrete Fourier transform to the synthetic complex gene expression profile dataset; and outputting data representative of the real-valued synthetic gene expression profile dataset for downstream biological / pharmacological analysis, processing and / or applications.

Aspects of the present invention relate to a method of determining a gene regulatory system within a cell that transitions from a first state to a second state including providing, to a neural network, a set of single-cell data of a target cell that is transitioning from a first state to a second state, calculating, via the neural network, a continuous trajectory of the target cell from the first state to the second state based on the single-cell data set, and interpolating a gene regulatory system of the target cell based on the calculated continuous trajectory, wherein the gene regulatory system includes a gene expression profile of at least one gene and at least one transcription factor that regulates expression of the at least one gene. Further, a system for determining a gene regulatory profile of a cell comprising at least one neural network is described.

The present invention relates to the technical field of cloud platform construction, and in particular to a method for constructing an intravenous treatment management cloud platform. The method comprises the following steps: performing gene expression pathway enrichment analysis on the patient's venous system through gene sequencing technology, thereby obtaining gene expression data; performing gene expression spectrum analysis on the gene expression data and performing intravenous treatment response gene labeling, thereby obtaining a treatment response gene labeling set; using cloud computing technology to build an elastic computing resource scheduling system, thereby obtaining resource scheduling system data; performing node load balancing weight scaling based on resource scheduling system data and gene expression data, thereby obtaining resource balancing scaling data; performing multi-scale pyramid feature refinement on gene expression data, thereby obtaining gene image feature refinement data. The present invention realizes the method for constructing an intravenous treatment management cloud platform through node load balancing weight scaling and GPU heterogeneous computing acceleration processing.

The invention discloses an application of a GmEF9 gene in enhancing soybean nematode resistance. The application of the GmEF9 gene or a biological material related to the GmEF9 gene in at least one of the following aspects: A1) regulating and controlling the soybean nematode resistance or preparing a product for regulating and controlling the soybean nematode resistance; a2) cultivating soybeans with improved nematode resistance or preparing products for improving the nematode resistance of the soybeans; a3) preparing transgenic soybeans; the nucleotide sequence of the GmEF9 gene is as shown in SEQ ID NO. 1. The gene GmEF9, which is remarkably and highly expressed in syncytial bodies of disease-resistant varieties, is successfully identified by comparing and analyzing gene expression profiles of the disease-resistant and susceptible soybean varieties in SCN-infected early-stage root tissues by utilizing a single cell nucleus transcriptome technology. The over-expression of GmEF9 can significantly enhance the SCN resistance of infected soybeans and effectively inhibit the development of nematodes in roots (J3-J4 stage). The research not only provides a new key gene resource for soybean anti-SCN breeding, but also provides a method example for analyzing a plant-pathogen interaction mechanism by using a leading-edge single cell technology.