93 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 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 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 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.

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 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 application is based on a blood vessel risk assessment model system based on a multi-modal fusion strategy and deep learning: an acquisition module acquires examination and test data, image data, gene detection data and clinical data of a plurality of historical patients, and labels various blood vessel disease conditions; a preprocessing module standardizes numerical data, one-hot encodes or entity embedding of category data, converts time series data into fixed length vectors, extracts imaging features from image data and / or learns deep image features from the image data, extracts features from text data, reduces SNPs data, normalizes gene expression profile data and selects features; a feature sample construction module constructs a training set and a validation set using the multi-modal preprocessed features of each historical patient; a model construction module constructs a multi-task deep learning model; a multi-task learning training module trains and validates the model, optimizes the hyperparameters of the model using an optimization algorithm, and adjusts the hyperparameters to the optimal to obtain a target multi-task deep learning model.

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 disclosure includes the identification and use of gene expression profiles, or patterns, with clinical relevance to cancer. In particular, the disclosure includes the identities of genes that are expressed in correlation with tumor grade. The levels of gene expression are disclosed as a molecular index for determining tumor grade in a patient and predicting clinical outcome, and so prognosis, for the patient. The molecular grading of cancer may optionally be used in combination with a second molecular index for diagnosing cancer and its prognosis. The disclosure further includes methods for predicting cancer recurrence, and / or predicting occurrence of metastatic cancer. For diagnosis or prognosis, the disclosure further includes methods for determining or selecting the treatment of cancer based upon the likelihood of life expectancy, cancer recurrence, and / or cancer metastasis.

The present disclosure provides gene expression profiles that are associated with cancer, including certain gene expression profiles that differentiate between cancer that is at a high risk of recurrence. The gene expression profiles can be measured at the nucleic acid or protein level. The gene expression profiles can also be used to identify a subject for cancer treatment. Also provided are kits for use in predicting cancer recurrence and / or prognosing cancer and an array comprising probes for detecting the unique gene expression profiles associated with cancer.