47 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 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 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.

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.

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 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 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.

The present invention relates to methods for predicting the progression of prostate cancer, specifically metastatic castration-resistant prostate cancer (mCRPC), in an individual. The invention employs gene expression profiling combined with predictive modeling to assess the likelihood of overall survival and biochemical recurrence following treatment.

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 application relates to a spatial transcriptome gene expression prediction method. The method includes: acquiring stained pathological image data of a target tissue; dividing the stained pathological image data into image blocks to obtain at least two image blocks to be identified; for each image block, determining a first cross-modal image feature of the image block and a second cross-modal image feature of the adjacent image blocks; predicting the image block based on the aggregation result of the first and second cross-modal image features to determine the gene expression prediction result and cell type prediction result corresponding to the image block; and generating a spatial transcriptome gene expression prediction map of the target tissue based on the gene expression prediction result and cell type prediction result of each image block. This method can improve the prediction accuracy of gene expression profiles.

The present invention relates to a method of predicting the outcome of a subject suffering from glioma comprising determining or receiving the outcome of determining a gene expression profile 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 medical caregiver or subject.

The invention discloses an adverse drug reaction prediction method, system, equipment and medium, and relates to the technical field of drug response prediction.The method comprises the steps that tumor cell-drug data and a gene expression profile of a tumor cell line are obtained; the method comprises the following steps: constructing a heterogeneous bipartite graph by taking a tumor cell line and a drug in tumor cell-drug data as nodes and based on sensitive and drug-resistant edges defined by response values of tumor cell line-drug pairs; for nodes in the heterogeneous bipartite graph, based on a gene expression profile of a tumor cell line, cell gene expression characteristics are extracted through inductive learning, and drug characteristics are generated by fusing self characteristics of drugs and neighbor information of the drugs in the heterogeneous bipartite graph; and carrying out feature splicing on the drug features and the cell gene expression features to obtain each tumor cell-drug reaction pair, and carrying out prediction. According to the method, precision and generalization-technology secret details are balanced through direct push-inductive fusion learning, and prediction of new cell-new drug pairs is realized.

Provided is a method of predicting a condition of a patient, including decomposing an input matrix to produce a residual matrix, the input matrix representing patients and expression levels of genes of the patients, training a classifier by using health condition values the patients as learning criteria and inputting the residual matrix into the classifier, and obtaining a value for predicting a health condition of a first patient, by inputting a first input matrix including the expression levels of genes of the first patient into the classifier, the first residual matrix being a residual matrix obtained from the first input matrix by using the predetermined algorithm.

The invention relates to a token gating generation type refining model for high-fidelity space transcriptomics analysis and robust spatial domain clustering. The problems that in the prior art, a spatial transcriptome data calculation and analysis method is difficult to effectively process sparse data, long-range spatial dependence cannot be efficiently modeled, and spatial domain recognition precision is limited are solved. The method comprises the following steps: S1, data acquisition: acquiring transcriptome data; s2, data preprocessing: selecting a hypervariable gene and carrying out normalization processing on an expression profile of the hypervariable gene; s3, model construction: reconstructing the extracted features through the three core modules; the method is a joint loss function based on a graph convolutional network and zero-expansion negative binomial distribution, a diffusion model architecture with UGate as a trunk and a mean value reconstruction method based on similarity guidance. And S4, performing performance evaluation and optimization. The method has the advantages that sparse data can be effectively processed, the long-range spatial dependency relationship is efficiently captured, and the precision and robustness of spatial transcriptomics data analysis are improved.

The application provides a gene expression profile classification method and system based on feature dependence and multi-objective particle swarm optimization feature selection, comprising the following steps: calculating the dependence score of each feature of the gene expression profile data, quantifying the dependence relationship between features by information entropy and mutual information; initializing the particle swarm based on the dependence score, and preferentially selecting features with high dependence scores; adopting a multi-objective particle swarm optimization algorithm for feature selection, combining the classification error rate and the feature selection rate as the objective function, and realizing the balance between low error rate and feature subset selection in the optimization process; adopting an improved particle optimal position updating strategy, generating new solutions through a non-dominated solution updating mechanism based on cubic spline interpolation, and improving the global search ability of the particle swarm; and obtaining the optimal feature subset through the multi-objective optimization process. Through the optimization of the feature selection process and the improvement of the particle swarm optimization strategy, the application improves the classification performance and the efficiency of feature selection, and can effectively capture the dependence relationship between genes.

The method of generating multipotent stem cells is a method for producing and / or expanding multipotent stem cells by delivering at least one reprogramming protein into somatic cells. The at least one reprogramming protein includes a Master Regulator (MR) protein, which may be BAZ2B, ZBTB20, ZMAT1, CNOT8, KLF12, DMTF1, HBP1, or FLI1. The bromodomain protein BAZ2B, in particular, was identified by first generating bi-species heterokaryons by fusing Tcf7l1− / − murine embryonic stem cells (ESCs) with human B-cell lymphocytes. Reprogramming of the B-cell nuclei to a multipotent state was tracked by human mRNA transcript profiling at multiple timepoints. Interrogation of a human B-cell regulatory network with gene expression signatures collected from such reprogramming time series identified eight candidate Master Regulator proteins, which were validated in human cord blood-derived hematopoietic progenitor and lineage-committed cells.

The invention relates to the technical field of biology, in particular to a racoon ussuriensis dog skin hair follicle single cell transcriptome map and a construction method thereof. The construction method comprises the following steps: collecting a skin tissue sample in the middle of the back of a male racoon ussuriensis dog in a winter hair period, dissociating and preparing a single-cell suspension, constructing a cDNA library and sequencing; comparing original sequencing data to a reference genome, and quantifying high-throughput single cell transcriptome data; further quality control is carried out, low-quality cells and double cells are removed, and standardization processing is carried out on data; screening hypervariable genes, and performing dimensionality reduction and visualization on a gene expression profile to obtain marker genes of a cell population; and calculating the correlation between the to-be-identified cell expression profile and the reference data set, and identifying the cell type. Based on a single cell transcriptome sequencing technology, the cell heterogeneity problem which cannot be solved by tissue sample sequencing is solved, a new way is provided for exploring a hair follicle development heterogeneity mechanism, and a new direction is also provided for biological research of racoon dog hair follicles.

The invention provides a gene expression profile bi-clustering method based on a swarm intelligence algorithm, and belongs to the technical field of gene expression, and the method comprises the following steps: constructing an index matrix for a gene expression data matrix, obtaining a bi-clustering seed, initializing a population, taking the bi-clustering seed as a first population individual, and constructing a second population individual; adding the first population individuals into a second population for population expansion to obtain two population individuals, evaluating the first population individuals by using a fitness function, and evaluating a plurality of individuals which contribute to the second population to the greatest extent by the individuals in the first population until a preset maximum number of evolution times is reached; and selecting an optimal bi-cluster from the second population. The index matrix is constructed for the gene expression data matrix, so that later data processing is simpler and higher in efficiency, meanwhile, data values of each gene under each condition can be searched by using the swarm intelligence algorithm, and therefore, the search range is wide, and important biological information is not easy to miss.

A degraded sequencing data sample may be generated to include one or more artificial zero expression values by replacing one or more expression values in a sequencing data sample with the one or more artificial zero expression values. An imputation model may be trained based on the degraded sequencing data sample before being applied to correct excess zero expression values in one or more gene expression profiles. The training of the imputation model may include leveraging the output of the imputation model to infer true zero expression values in the sequencing data sample for supervising further training of the imputation model. The imputation model may be trained to replace spurious zero expression values with an imputed expression value that is as close as possible to the original expression values while preserving any true zero expression values and non-zero expression values.

The application provides a method for long-term in-vitro culture and expansion of primary hepatocytes and application thereof. The method for in-vitro culture and expansion of primary hepatocytes comprises using a culture medium containing interleukin-22 (IL-22), combined with hepatocyte growth factor (HGF) and / or epidermal growth factor (EGF) to induce primary hepatocytes to prepare proliferative liver precursor cells. The application combines interleukin-22 (IL-22), hepatocyte growth factor (HGF) and / or epidermal growth factor (EGF) to induce in-vitro mass expansion of primary hepatocytes, and through the method, mature-like hepatocytes with a gene expression profile extremely similar to that of primary hepatocytes and perfect functions can be obtained, and the hepatocytes expanded by the method have good in-vivo therapeutic effects.

The invention provides an application of post-exercise induced tears in preparation of a pharmaceutical composition for treating or preventing myopia, which is characterized in that the tears (TA) of a healthy individual after exercise are collected and subjected to standardization treatment to serve as an active ingredient to prepare an ophthalmic preparation. Collecting tears based on the motion state difference, carrying out standardization treatment, and treating the A549 cells; taking the complete tear as a transcriptome perturbation factor, and obtaining a gene expression profile through high-throughput sequencing; analyzing and predicting a tear function by using reverse transcriptomics; and finally verifying the biological effect through an animal model. Experimental results show that after-exercise tear-induced gene characteristics are in significant negative correlation with myopia-related disease expression profiles, and in a form deprivation animal model, TA can effectively inhibit ocular axis growth (plt, 0.001) and diopter progress (plt, 0.01). The invention reveals for the first time that movement mediates ocular axis regulation by changing the tear function, and has the advantages of non-invasiveness, high safety, large transformation potential and the like.

The invention relates to the technical field of gene screening, in particular to a drug-resistant key gene screening method and device, electronic equipment and a storage medium, and the method comprises the following steps: constructing a drug-resistant gene extraction task for obtaining a drug-resistant gene expression profile in an mRNA expression profile by using a first neural network, constructing a drug-resistant key gene extraction task for obtaining a drug-resistant key gene expression profile from a drug-resistant gene expression profile by using a second neural network, and carrying out combined learning on the drug-resistant gene extraction task and the drug-resistant key gene extraction task in a multi-task learning mode, and constructing a drug resistance key gene screening task for obtaining a drug resistance key gene expression profile in the mRNA expression profile. According to the method, the mastering accuracy of the gene expression level can be improved, so that the gene screening accuracy is improved, and the accuracy of the finally obtained drug-resistant key gene is improved.

A small molecule cross-modal generation method based on scGPT encodes and extracts features from the preprocessed gene transcription profile and small molecule data using a gene encoder and a molecule encoder respectively, maps the two modalities to the same dimensional space using a fully connected layer, and performs multi-modal matching of the gene modality and the small molecule modality through contrast learning, aligns the gene vector through an autoregressive model to convert the gene modality to the small molecule modality, and finally outputs the small molecule vector in SMILES and Mol formats. The present application obtains candidate molecules capable of inducing the required transcriptome profile by inputting the required gene expression profile and the control gene expression profile.

The present invention relates to the technical field of drug repositioning, and provides a method for identifying and predicting a repositioned drug for protection against γ-ray radiation damage. The method comprises: extracting a target gene expression profile dataset of a target biological effect and a candidate gene expression profile dataset of a candidate repositioned drug; calculating an ES of the target gene expression profile dataset and an ES of the candidate gene expression profile dataset; and on the basis of an inverse biological effect and the ESs, selecting, from the candidate gene expression profile dataset, a corresponding candidate drug for protection against the target biological effect. The present invention further reduces the time and economic costs for discovery of the repositioned drug for protection against radiation, and also improves the success rate.

A training dataset may be generated from a set of sample gene expression profiles associated with different tissue types, cell types, and / or diseases. An embedding computation model may be trained based on the training dataset. A gene expression profile of a patient may include a gene expression level of a plurality of genes expressed by different cells. The embedding computation model may be applied to generate a multicellular representation of one or more subsets of cells from the gene expression profile of the patient. The embedding computation model may generate the multicellular representation by aggregating the cell embeddings generated by embedding the gene expression level of each cell in the subset of cells. One or more downstream tasks, such as dimensionality reduction, biological feature prioritization, treatment response prediction, disease severity classification, and patient subgroup discovery, may be performed based on the multicellular representation of the patient.