111 results about "Gene expression profiling" patented technology

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 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.

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 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 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 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 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.

This invention relates to a method for assessing the quality of single-cell sequencing data, which addresses the current difficulty in evaluating the differences in data quality after applying different single-cell sequencing data imputation algorithms without the participation of real samples. The method includes the following steps: First, two single-cell sequencing data imputation algorithms are prepared. Then, a synthesis matrix based on the statistical characteristics of real data is created and normalized preprocessed. The normalized gene expression matrix is ​​input into the two imputation algorithms to be evaluated, and the output feature vectors are extracted to set an optimization function. The gene expression matrix is ​​optimized using the optimization function, and then the optimized matrix is ​​denormalized to obtain the imputed gene expression profile. Finally, the obtained gene expression profile is input into the two algorithms to be evaluated to obtain two sets of predicted feature vectors. The data quality difference value can be calculated using these feature vectors. This invention can accurately assess the data quality difference between two data imputation algorithms without the participation of real samples.

The invention provides a method for genetic analysis in individuals that reveals both the genetic sequences and chromosomal copy number of targeted and specific genomic loci in a single assay. The present invention further provides methods for the sensitive and specific detection of target gene sequences and gene expression profiles.

The embodiments of the present application provide a method, system, electronic device and storage medium for processing genetic data, which belongs to the field of genetic analysis technology. The method obtains the genotype data of the individual to be tested, and determines multiple associated genes of the individual to be tested based on the genotype data and the single nucleotide polymorphism data associated with the target risk task; queries the associated gene features corresponding to each associated gene in the pre-constructed gene feature library, and the gene feature library includes embedded representations of different genes constructed based on the embedding model; the embedding model is obtained by self-supervised learning of multiple gene expression profiles of the same species as the individual to be tested; integrates the features of multiple associated gene features to obtain individual features; calls the prediction model to process the individual features to obtain the risk score corresponding to the individual to be tested and the target risk task. This method can improve the accuracy of genetic data processing.

This provides a molecular diagnostic tool useful for the efficient and accurate characterization of the discriminative and functional properties of iPSC-derived dopaminergic neurons. [Solution] A computer-based method for identifying determined dopaminergic progenitor cells within an in vitro population of neural progenitor cells is provided, comprising: receiving a test dataset containing data such as gene expression profile information relating to an in vitro population of neural progenitor cells; querying a gene expression reference database and comparing the test dataset with the gene expression reference database, wherein the gene expression reference database contains gene expression profile information of the desired determined dopaminergic progenitor cells; and outputting a computer-calculated label classification that includes an indication of whether or not the in vitro population of neural progenitor cells contains the determined dopaminergic progenitor cells.

The disclosure relates to a method for assessing prostate cancer progression, aggressiveness, and therapy outcomes by employing transcriptome-based metrics. The disclosure also relates to a method of computing a Stemness score by correlating a prostate cancer sample's gene expression profile with a stem cell signature derived from a machine learning algorithm. The disclosure further relates to a method of calculating a PCa-Stem signature score by performing single-sample gene set enrichment analysis on at least twelve genes, including HMMR, AURKB, CENPA, DEPDC1B, HJURP, PBK, MELK, UBE2C, DLGAP5, NEK2, BIRC5, and KLK12. The method may also involve comparing the Stemness and PCa-Stem signature scores to clinical cohort benchmarks to determine prostate cancer stage, aggressiveness, or risk category. Prostate cancer samples characterized by high Stemness or PCa-Stem signature scores may indicate aggressive disease and correlate with poor therapy outcomes.

The disclosure includes the identification and use of gene expression profiles, or patterns, with clinical relevance to extended treatment and cancer-free survival in a patient. In particular, the disclosure includes the identities of genes that are expressed in correlation with benefit in a switch in endocrine therapy used to treat a patient. The levels of gene expression are disclosed as a molecular index for predicting clinical outcome, and so prognosis, for the patient. The disclosure further includes methods for predicting cancer recurrence, and / or predicting occurrence of metastatic cancer, after initial treatment with an anti-estrogen agent. The disclosure further includes methods for determining or selecting the treatment of a subject based upon the likelihood of life expectancy, cancer recurrence, and / or cancer metastasis.

This invention discloses a single-cell incremental annotation method based on distribution and expression-aware revisiting, belonging to the technical field of single-cell type annotation. The method includes: inputting the single-cell expression profile sample to be processed into a diffusion model based on distribution-aware conditions to generate gene expression profile data of old cell samples that participated in the diffusion model training; integrating the gene expression profile data of the old cell samples with the gene expression profile data of newly collected cell samples; and inputting the integrated data into two different feature extractors in an expression-aware knowledge distillation model to perform multi-view gene expression alignment and output the corresponding cell type annotation results. This invention effectively solves the core challenge of incremental annotation of long-tailed and high-dimensional sparse single-cell data, enabling efficient data replay and continuous learning, thereby significantly improving the performance and robustness of incremental annotation.

The present invention relates to a method for predicting the outcome of a subject having a glioma, comprising the steps of determining a gene expression profile or receiving the result of determining a gene expression profile, wherein the gene expression profile is determined in a biological sample obtained from the subject, determining a prediction of the outcome based on the gene expression profile, and optionally providing the prediction to a healthcare provider or the subject.

The invention relates to a molecular structure reconstruction method and system based on a gene expression profile, and belongs to the crossing field of computer-aided drug design and artificial intelligence. According to the method, a phenotype-perceived molecular generation model is constructed, and a partially deleted or masked molecular structure is reconstructed by using transcriptome data obtained after drug disturbance. The system comprises a convolution encoder used for extracting phenotypic features from an input drug-induced gene expression profile; the invention also relates to a decoder with a Transform structure, which is pre-trained on large-scale chemical structure data (more than two millions of molecules) so as to realize effective reduction of masked molecular fragments. The methods are trained and validated based on an L1000 public data set comprising more than 74,000 drug perturbed samples of 117 cell lines. In the training process, part of structures input by the molecules are randomly masked, and a model is trained to perform structure reconstruction by taking corresponding gene expression response as a condition, so that a corresponding relation between biological phenotypic characteristics and chemical structure sub-fragments is established. The invention provides a generative method for connecting a gene expression space with a chemical structure space, has good adaptability and interpretability, and provides a new technical path for phenotype-driven molecular design.

The present invention relates to a prognostic risk prediction model for colorectal cancer. The model is constructed through the following steps: obtaining the gene expression profiles and clinical information data of colorectal cancer patients, and the data for model construction and validation includes a training set and a validation set; performing differential expression analysis on the gene expression matrix to obtain differentially expressed genes; performing univariate Cox regression analysis on the differentially expressed genes in the training set to screen out prognosis-related genes; performing LASSO Cox regression analysis on the screened genes, determining the optimal λ value through cross-validation, screening out the genes that make up the model, and calculating the risk scores of each patient according to the regression coefficients of the genes and their expression levels in the training set and the validation set; dividing the patients into high- and low-risk groups according to the risk scores, and evaluating the prediction performance of the prediction model through Kaplan-Meier survival curves, time-dependent ROC curves, etc. The present invention can bring new strategies for predicting the survival time of colorectal cancer patients and the sensitivity to chemotherapeutic drugs.

The present invention discloses a method, device and medium for enhancing gene expression interaction in single-cell RNA sequencing data, and belongs to the field of data processing technology. The method first performs principal component analysis on the cell-gene expression spectrum matrix to obtain characteristic genes that can indicate cell differences, calculates cell distance based on characteristic genes, obtains cell similarity based on cell distance, further obtains Markov transition probability based on cell similarity, and performs multiple interpolation on the cell-gene expression spectrum matrix. The method of the present invention can eliminate expression noise in the cell-gene expression spectrum matrix and fill in the missing expression, and finally can effectively enhance gene expression interaction, further can improve the clustering effect of cells, and effectively perform cell typing.

This invention provides skin cell fibroblast- and blood-based methods for determining whether a human subject has a gene expression profile characteristic of AD. This invention also provides related methods for determining whether a demented human subject is afflicted with AD or non-ADD, and for determining whether a non-demented human subject has an increased likelihood of becoming afflicted with AD.

The invention relates to the technical field of agricultural biology, and discloses a plant feed genetic marker FTIR spectrum screening method which comprises the following steps: firstly, acquiring feed samples of a plurality of plant individuals from a plant population with genetic diversity, and collecting FTIR spectrum data of the feed samples; preprocessing the spectral data and extracting quantified spectral characteristic parameters, thereby establishing a spectral phenotype for each plant individual; acquiring genetic information of the plant individuals, wherein the genetic information comprises genetic locus data or gene expression profile data; and finally, carrying out correlation analysis on the established spectral phenotype and corresponding genetic information to identify one or more genetic markers significantly related to the spectral phenotype. Efficient nondestructive evaluation of the chemical quality of the plant feed is achieved through the FTIR spectrum technology, related genetic markers are accurately identified in combination with genetic information, and an effective way is provided for accelerating breeding of high-nutritive-value feed varieties and revealing a molecular mechanism of quality formation.

The application provides a virtual organ simulation method and system based on three-dimensional stacked spatial transcriptome data, which comprises the following steps: constructing a spatial adjacency network according to the coordinate information of a plurality of spatial transcriptome slice data; using a graph attention autoencoder to perform expression embedding representation learning on each point in the spatial adjacency network; voxelizing the three-dimensional coordinates of each slice, constructing a continuum through Gaussian smoothing, and generating a three-dimensional morphological model; training a latent space diffusion model to generate a latent expression of the spatial transcriptome data; for a given coordinate and condition, sampling an embedding vector from the trained latent space diffusion model, and inputting the embedding vector into the graph attention autoencoder to obtain the gene expression profile of the corresponding point. The advantage of the application lies in that the spatial transcriptome of the whole organ or even the cross-tissue region is uniformly modeled and expressed, which breaks through the small-scale modeling capability of the existing method which is limited to a local region or a single slice.

The invention discloses a single cell increment annotation method based on distribution and expression perception return visit, and belongs to the technical field of single cell type annotation. Comprising the following steps: inputting a single cell expression profile sample to be processed into a diffusion model based on a distribution perception condition, and generating gene expression profile data of an old cell sample participating in diffusion model training; integrating the gene expression profile data of the old cell sample and the collected gene expression profile data of the new cell sample; and respectively inputting the integrated data into two different feature extractors in the knowledge distillation model for expressing perception so as to carry out multi-view gene expression alignment, and outputting a corresponding cell type annotation result. According to the method, the core problem of long-tail distribution and high-dimensional sparse single cell data in incremental annotation can be effectively solved, data playback and continuous learning can be efficiently realized, and thus the performance and robustness of incremental annotation are remarkably improved.

The invention discloses application of DDC silencing in an encephalatrophy model, and relates to the field of traumatic brain injury. According to the application, by inhibiting DDC gene expression or protein activity, GSK-3beta kinase mediated Tau protein phosphorylation is regulated and controlled, so that encephalatrophy is relieved or prevented; wherein the encephalatrophy model is an immature brain traumatic brain injury (TBI) after-encephalatrophy model, and DDC silencing is achieved through siRNA, shRNA or antisense oligonucleotide. A gene expression profile after immature brain TBI is analyzed through transcriptome sequencing, DDC is found to be remarkably up-regulated and enriched in a 5-hydroxytryptamine synaptic pathway, an in-vivo experiment proves that DDC expression and Tau phosphorylation level are synchronously increased, an in-vitro experiment shows that DDC silently inhibits GSK-3beta-mediated Tau phosphorylation, and a basis is provided for a DDC-targeted treatment strategy.

The invention discloses a gene expression profile prediction method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. Specific genetic variation and regulation region sequences in a peripheral blood sample are detected, and a standardized SNP genotype matrix and a regulation annotation vector are obtained; constructing a gene-pathway-disease-drug four-layer regulation and control network based on multi-source heterogeneous data, and determining a core node gene based on the constructed network; obtaining LD structure information and chromatin accessibility characteristics corresponding to the core node gene; and taking the standardized SNP genotype matrix corresponding to the core node gene, the regulation and control annotation vector, the LD structure information and chromatin accessibility characteristics as input of a pre-trained gene expression profile prediction model to obtain a gene expression profile prediction value of an individual corresponding to the peripheral blood sample in a specified brain region. Therefore, an accurate mapping relation between the blood gene expression data and the gene expression data of the targeted CNS tissue is established.