1765 results about "Expression gene" patented technology

Provided herein are methods of detecting a target nucleic acid of interest for spatial gene expression analysis in a sample using barcoded templated ligation probes.

Provided herein are devices, methods, and systems for in situ gene sequencing of a target nucleic acid in a cell in an intact tissue. Methods of screening a candidate agent to determine whether the candidate agent modulates gene expression of a nucleic acid in a cell in an intact tissue are also provided herein.

Provided herein are methods for analyzing a biological sample that include contacting the biological sample with a non-conductive substrate; and contacting a matrix to a surface of the biological sample. Further methods optionally include performing mass spectrometry analysis; determining presence of a first analyte in the biological sample; and / or analyzing a second analyte on the surface of the biological sample to determine presence of the first and the second analyte in the biological sample.

The invention discloses a drug resistance prediction method and system based on comparative learning and multi-modal fusion, and the method comprises the steps: firstly generating a molecular map and a molecular fingerprint based on the SMILES of a target drug, and extracting the molecular features of the drug through a comparative learning model constructed through combining a map attention network and a map convolution network; and then, acquiring protein expression, gene expression and metabolic expression data from the target tissue cells, extracting modal features through a deep convolutional network, a Transform encoder and a multi-dimensional attention network, and realizing adaptive fusion of the multi-modal features through a heterogeneous interactive attention mechanism. And finally, jointly inputting the fused multi-modal features and drug molecular features into a multi-layer sensor to realize high-precision prediction of the drug resistance of cells to drugs. By introducing a contrast learning and multi-modal feature fusion mechanism, the characterization capability and prediction precision of the model are effectively improved, and efficient and reliable support can be provided for drug screening and clinical decision making.

The invention provides an artificial nucleic acid molecule which is used for improving the expression quantity of target amino acid, polypeptide or protein. The artificial nucleic acid molecule at least comprises a target 5'untranslated region (UTR), a target coding region (CDS) and a target 3 'untranslated region (UTR). Wherein the sequence of the target 5 'UTR is one of the following sequences: 5' UTR of a high-expression gene and a 5 'UTR variant of the high-expression gene. The sequence of the target 3 'UTR is one of the following sequences: 3' UTR of a high-expression gene and a 3 'UTR variant of the high-expression gene. Optionally, the artificial nucleic acid molecule may further comprise, for example, a 5 '-end cap structure (Cap), a PolyA tail. The 5 'UTR and the 3' UTR have regulating effects on translation and stability of nucleic acid molecules, so that the 5 'UTR, the 3' UTR and variants thereof are selected from high-expression genes, the nucleic acid molecules can be further stabilized and are not easy to degrade, and the amount of protein or polypeptide obtained by translation of the nucleic acid molecules can be increased. The invention also provides methods for making, delivering, and using such artificial nucleic acid molecules, as well as the use of the artificial nucleic acid molecules for the treatment and / or prevention of related diseases or disorders.

The invention provides a key node identification method for a disease marker expression regulation network, and belongs to the technical field of disease markers, and the method comprises the steps: firstly carrying out the preprocessing and quality control of original data, including batch effect removal, abnormal sample identification and the like; then identifying differential expression genes through multiple difference analysis and a pre-training model, and constructing a gene expression correlation network; and integrating multi-source regulation and control data to construct a multi-level weighted network, calculating network node features, and carrying out representation learning and module division. And based on multi-dimensional features such as network topology features, module contribution degree and biological importance, a neural network model is trained to carry out key node identification. And finally, optimizing the model through multi-layer verification such as pathway enrichment, disease gene overlapping, expression stability, time sequence change and network disturbance, and finally obtaining a verified key node set. The problem that in the prior art, the interaction relation between molecules is ignored, and consequently some key regulation and control nodes are possibly missed is solved.

The invention discloses a genomics cross-modal condition generation method and system based on a potential diffusion model, and belongs to the field of bioinformatics, and the method comprises the following steps: respectively designing and constructing an auto-encoder for gene expression and gene mutation data, and carrying out model training; constructing a potential feature space according to an encoder of gene mutation data and training an unconditionally guided potential diffusion model; constructing a cross-modal feature alignment network according to the two trained encoders and training the cross-modal feature alignment network; and performing joint fine tuning on the unconditionally guided potential diffusion model and the cross-modal feature alignment network. According to the method, the problem of sample modal deletion in multi-modal learning is solved, potential correlation between gene expression data and gene mutation data can be captured through cross-modal feature alignment, information flow between different modals is enhanced, and high-quality prediction data is effectively generated when mutation data is deleted through the potential diffusion model.

The invention discloses a multi-omics causal structure relation learning method based on comparative learning, which comprises the following steps: firstly, respectively constructing corresponding encoders for preprocessed gene mutation and gene expression data, and respectively carrying out feature extraction on two kinds of omics data; then, constructing a projection head with shared parameters to realize cross-modal feature alignment; then, using the aligned features as nodes, and constructing causal graph data through a learnable causal graph structure; constructing a graph neural network to learn causal graph representation, and constructing a contrast loss function; and finally, a model prediction result is obtained through a multi-layer perceptron, a survival prediction loss function is constructed, and a total loss function is obtained for multi-omics causal structure model training. Based on gene mutation and gene expression data, a cross-omics causal structure relationship is constructed and learned through comparative learning, more accurate prognosis prediction is provided for diseases such as acute myelogenous leukemia and the like, and potential biomarkers and key regulatory factors are helped to be found.

The invention provides an UTR element NHP1 as well as a construction method and application thereof, and relates to the technical field of mRNA. A 5 'UTR with a good expression effect is designed by integrating dominant sequences of a human high-expression gene and a pathogen natural UTR, a chimeric structure NHP1 with high ribosome load is predicted through a calculation model, a DNA sequence of the NHP1 is as shown in SEQ NO 1, and an RNA sequence of the NHP1 is as shown in SEQ NO 2; an EGFP report system is adopted on the DNA level to rapidly screen UTR; the translation efficiency is quantitatively evaluated on the RNA level through luciferase mRNA (N1-methyl pseudouridine modification); and the particle size is controlled by a microfluidic technology, so that the optimized UTR-mRNA is efficiently expressed after being delivered.

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

The invention discloses a breast cancer prognosis real-time evaluation system and method fusing a multi-modal image and a co-disease network, and relates to the technical field of breast cancer prognosis evaluation. According to the system, a comprehensive feature matrix fusing images, genes and clinical features is constructed by acquiring a mammary gland medical image, extracting focus features and combining gene expression information of a focus area and co-disease data of a patient, feature weighting is carried out based on an attention mechanism, and finally a prognosis risk score is output by utilizing a multi-layer perceptron model. And risk grading and intervention suggestion generation are realized. According to the system, a co-disease network and a gene interaction network are introduced for modeling, relevance expression among multi-source information is enhanced, and pathological features and systematic health status of patients can be reflected comprehensively. The intelligent level of prognosis evaluation can be improved, and good clinical popularization value is achieved.

The invention belongs to the technical field of plant genetic engineering, and particularly relates to an SCRE10 gene for improving rice disease resistance and application of the SCRE10 gene, the base sequence of the SCRE10 gene is shown as SEQ ID NO.1, and the amino acid sequence of the SCRE10 gene is shown as SEQ ID NO.2. Through construction of a dexamethasone induced expression SCRE10 transgenic rice plant, it is found that the transgenic rice can significantly induce PR gene expression and active oxygen outbreak, and the resistance of rice to false smut and bacterial leaf blight can be improved. The invention proves that heterologous inducible expression of the Ustilaginoidea virens SCRE10 gene has the function of positively regulating the disease resistance of the rice, and the SCRE10 gene can be used for improving the disease resistance of the rice, which is of great significance to the creation of disease-resistant germplasm of the rice.

The present disclosure relates to compositions and methods for reducing expression of MYC gene in a cell. In some embodiments, an expression repressor comprises a targeting moiety that binds a MYC promoter, anchor sequence, or super-enhancer. In some embodiments, the expression repressor comprises an effector moiety that represses transcription or methylates DNA. Systems comprising two expression repressors are also disclosed. The compositions can be used, for example, to treat cancers such as HCC.

The invention relates to the technical field of collaborative prediction, in particular to a plant polygene stress resistance collaborative prediction method and system based on a federal map neural network. The method comprises the following steps: modeling a multilayer heterogeneous graph according to acquired plant genome data; constructing a dynamic graph neural network model based on time sequence perception; training a dynamic graph neural network model by using the multi-layer heterogeneous graph, and performing distributed privacy calculation on the multi-layer heterogeneous graph by using a federated graph neural network; generalization training is carried out on the small sample scene data based on multi-task learning and a cross-species migration mechanism; and obtaining a gene function prediction result. Through a multi-layer heterogeneous graph fusion technology and a dynamic graph neural network design of time sequence perception, multi-dimensional biological information such as gene regulation, protein interaction, metabolic pathways and stress response can be captured at the same time, and time sequence change characteristics of gene expression in the plant stress response process can be captured at the same time.

Methods and systems for performing a clinical prediction are provided. In one example, the method comprises: receiving first molecular data of a patient, the first molecular data including at least gene expressions of the patient; receiving first biopsy image data of the patient; processing, using a machine learning model, the first molecular data and the first biopsy image data to perform a clinical prediction of the patient's response to a treatment, wherein the machine learning model is generated or updated based on second molecular data including at least gene expressions and second biopsy image data of a plurality of patients; and generating an output of the clinical prediction.

The invention discloses a breast cancer lymph node metastasis prediction system based on a gene spectrum, and relates to the technical field of breast cancer prediction systems. Comprising a data acquisition module which collects gene spectrum data of a breast cancer patient and collects detailed clinical information of the patient; the preprocessing module is used for carrying out data cleaning and data normalization processing on the collected data; and the feature extraction module is used for extracting principal component features by applying principal component analysis on the basis of the gene expression data. According to the method, gene expression data are considered, various gene spectrum data such as gene mutation and copy number variation and detailed clinical information are integrated, the biological characteristics of the breast cancer can be reflected more comprehensively, and the prediction accuracy is improved.

The embodiment of the invention relates to the technical field of bioinformatics, and provides a bulk data cell analysis method, device and equipment, and the method comprises the following steps: constructing an initial reference matrix according to a single cell data set and a cell type annotation template, each element in the initial reference matrix represents the gene expression quantity of each cell state under each characteristic gene; performing deconvolution on the bulk data to be analyzed according to the initial reference matrix to obtain a first deconvolution result; updating the initial reference matrix according to the first deconvolution result to obtain a first reference matrix; and according to the first reference matrix, performing deconvolution on the bulk data to be analyzed to obtain a second proportion and a second gene expression quantity of each cell type in the bulk data to be analyzed. According to the embodiment of the invention, the accuracy of cell analysis in bulk data can be improved.

The invention belongs to the technical field of plant genetic engineering molecular breeding, and particularly relates to application of wheat TabHLH93 protein or a coding gene thereof in regulation and control of plant grain development. According to the invention, a binary expression vector capable of realizing ectopic expression of the TabHLH93 gene is constructed and is genetically transformed into common wheat to obtain a progeny with high expression of the TabHLH93 gene, so that overexpression of the TabHLH93 gene is realized, and it is verified that the TabHLH93 has a function of regulating and controlling the size of wheat grains and also has an important regulation and control effect on agronomic characters such as wheat plant height, tiller number, spike length and thousand seed weight. A candidate gene with an important application prospect is provided for wheat high-yield genetic improvement, and the gene has important significance for guaranteeing grain safety.

The invention provides a screening method and application of an early prediction marker of papillary thyroid cancer, the early prediction marker of papillary thyroid cancer is P4HA2, application of a reagent for detecting the expression level of P4HA2 in preparation of a product is provided, and a P4HA2 inhibitor including a substance for knocking down P4HA2 gene expression is also provided. The invention also provides application of the P4HA2 inhibitor in preparation of a product for inhibiting migration and / or proliferation of thyroid cancer cells, and a screening method of a biomarker for predicting papillary thyroid cancer. The marker can be used for prediction and early prediction of papillary thyroid cancer, knock-down of the marker can also be used for prediction and early prediction of papillary thyroid cancer, early screening can be better completed, benign and malignant nodules are helped to be distinguished, unnecessary invasive detection is reduced, and the detection efficiency is improved. And a new view angle is provided for molecular mechanism research and early diagnosis of thyroid cancer.

The invention discloses a SbWRKY51 gene, a promoter and application of the SbWRKY51 gene and the promoter in improvement of salt tolerance of sorghum, and belongs to the technical field of plant genetic engineering. The SbWRKY51 gene is screened from sorghum, and the expression level of the SbWRKY51 gene is up-regulated after salt stress treatment. The protein coded by the gene belongs to a WRKY transcription factor family, has transcriptional activation activity, and can start the expression of a reporter gene. Sorghum overexpression strains and complementary strains of the gene are obtained, salt tolerance related physiological indexes are measured, overexpression of the gene can improve the seed germination rate, increase the seed root length, regulate ion balance and enhance the free radical scavenging capacity of plants, and the effect of the gene in the aspect of improving the salt tolerance is embodied. A further research finds that the SbWRKY51 gene can enhance the plant salt tolerance by regulating a lignin synthesis pathway. The technical scheme provides a basis for cultivating resistant plants, and has important guiding significance for enhancing the production potential of sorghum under a high-salt condition and promoting agricultural development.

The invention discloses a breast cancer recurrence risk prediction method and system based on multi-modal data missing interpolation and gene interpretability enhancement. The method comprises the following steps: firstly, dynamically generating and complementing features of a missing mode by matching a generative adversarial network with a mode missing mask matrix; then, a feature screening mechanism driven by gene information is introduced, through a multi-task learning network, image feature extraction is supervised by using a gene expression tag in a model training process, and image features highly associated with recurrence-related genes are screened out; and finally, fusing the complemented multi-modal time sequence characteristics by adopting Transform, and outputting a recurrence risk probability. According to the method, the robust prediction performance can be realized under the condition of data missing, and meanwhile, image interpretation with a molecular biology basis is provided for the feature screening process of the model, so that the reliability and clinical acceptability of the whole system are enhanced.

The invention belongs to the technical field of molecular biology, and particularly relates to a CeNF-YA3 gene, an expression vector and application of the CeNF-YA3 gene in vegetable fat regulation and control. The nucleotide sequence of the CeNF-YA3 gene is as shown in SEQ ID NO.1. After the CeNF-YA3 gene is over-expressed, the grease content of arabidopsis seeds and leaves can be remarkably increased; in addition, the invention also provides a series of vectors containing the CeNF-YA3 gene for subcellular localization, yeast hybridization and plant overexpression for the first time, and proves that the CeNF-YA3 protein has a transcriptional activation function, is localized in a cell nucleus and accords with the basic characteristics of transcription factors for the first time. Therefore, the technical scheme of the invention provides scientific guidance for the application of the CeNF-YA3 gene and the protein thereof.

The invention relates to a biological system critical state early warning method and device, equipment and a storage medium. The method comprises the following steps: acquiring gene expression data obtained by sampling a plurality of cells at different moments; respectively constructing a cell specificity directed network of each cell at each moment based on the gene expression data at each moment; extracting a local directed network of each gene from the cell specificity directed network of each cell at each moment; based on the extracted local directed network of each gene, respectively determining local directed network flow entropies of each gene for different cells at each moment; determining a global directed network flow entropy score at each moment according to each local directed network flow entropy at each moment; and determining the occurrence time of the critical state according to the change trend of the global directed network flow entropy score at different moments, and pushing an early warning message based on the occurrence time of the critical state. By adopting the method, the accuracy of critical state early warning can be improved.

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

The invention discloses a drug perturbation diffusion prediction method fused with cell description, and relates to the technical field of drug perturbation prediction.The method comprises the steps that firstly, a cell perturbation transcriptome database is preprocessed, and a cell-drug combination containing drug characteristics, cell line gene expression and cell line description characteristics is obtained to serve as training data; and then constructing a drug disturbance prediction diffusion model based on cell description, training the drug disturbance prediction diffusion model by using the training data, and finally inputting Gaussian white noise, cell line gene expression before disturbance, drug characteristics and cell line description characteristics into the trained model to predict cell line gene expression after drug disturbance. According to the method, cell line description characteristics are introduced, so that the perception capability of the model on intercellular biological differences is enhanced, and the generalization performance of cross-drug and cross-cell lines is improved.

The invention discloses application of an apple MdMYS1 gene in regulating and controlling the wax content of plant fruits and leaves, and belongs to the technical field of plant genetic engineering. According to the invention, the MdMYS1 gene with high expression quantity in apple varieties with more waxiness is separated, and the nucleotide sequence of the MdMYS1 gene is shown as SEQ ID NO. 1. Through subcellular localization, the transcription factor expressed by the MdMYS1 gene is found to be localized on a cell nucleus. Experimental results show that overexpression of the MdMYS1 gene can significantly increase the wax content of the apple fruits and leaves by promoting biosynthesis of wax, which indicates that the MdMYS1 gene plays a key role in regulation and control of the wax content of the apple fruits and leaves. The invention provides an efficient and rapid way for apple breeding, provides a gene material for improving apple quality, and has a wide application prospect in improving economic benefits and ecological benefits of apple planting.

Methods and compositions for treating fibrosis (e.g., cardiac fibrosis), or other conditions associated with inflammation and / or fibrosis are provided. Methods can include administering a nucleic acid encoding a sialic acid-binding immunoglobulin-type lectin 9 (Siglec-9) protein to a subject in need of fibrosis treatment, where the Siglec-9 protein is expressed by the nucleic acid in regulatory T (Treg) cells in the subject. Methods and compositions can include Treg cells that overexpress Siglec-9 to enhance the ability of Treg cells to target cells expressing amine oxidase, copper containing 3 (AOC3), including fibroblasts or myofibroblasts. In some embodiments, the enhanced Treg cells are targeted to myofibroblasts (e.g., cardiac myofibroblasts) with elevated expression of fibrotic genes.

The invention belongs to the technical field of plant genetic engineering, and discloses an application of a corn gene ZMM3 in control of corn yield, the gene is located on the ninth chromosome of corn and controls important yield traits of corn female ear row number and ear thickness, and a protein encoded by the gene is shown as SEQ ID NO.2. The gene is knocked out by using a CRISPR / Cas9 technology, gene expression is inhibited, the ear row number of corn can be increased, and a new gene resource is provided for corn yield improvement.

The invention discloses application of a cell surface adhesion molecule CD22 as a target spot in preparation of drugs for treating spinal cord injury related diseases. The change of gene expression in the glial scar formation process is represented by space transcriptome sequencing, and the specific expression of CD22 in the glial scar region is up-regulated. According to single cell sequencing, in-situ hybridization and immunohistochemistry, specific high expression CD22 of part of microglial cells in a glial scar area is found. CD22 is knocked out through a genetic means, and it is found that formation of glial scars after spinal cord injury is remarkably reduced through inhibition of CD22. Behavioral detection finds that the error rate of irregular horizontal ladders is remarkably reduced by inhibiting CD22, and fine movement recovery of hind limbs of mice after spinal cord injury is promoted. The siRNA specifically targeting CD22 is injected into the sheath, so that the expression of microglial cells CD22 is inhibited, the error rate of irregular horizontal ladders of hind limbs can be reduced, and the recovery of the fine movement function of the hind limbs of the mouse after spinal cord injury is promoted. The invention provides a new possibility for development of drugs for spinal cord injury and treatment of spinal cord injury.

The invention discloses an application of an sscle13g095080 gene and a protein coded by the sscle13g095080 gene in regulation and control of pathogenicity of sclerotinia sclerotiorum. The nucleotide sequence of the sscle13g095080 gene is as shown in SEQ ID NO: 1 (Sequence Identity Number 1). The invention discovers that the sclerotinia sclerotiorum sscle13g095080 gene is related to the pathogenicity of sclerotinia sclerotiorum, and the pathogenicity of sclerotinia sclerotiorum can be obviously reduced by knocking out the sclerotinia sclerotiorum sscle13g095080 gene Therefore, the sscle13g095080 gene can be used as an effective target for preventing and treating the sclerotiniose, compounds capable of preventing gene expression and protein expression, modification and positioning of the gene can be screened, occurrence of the sclerotiniose can be effectively controlled, and development of novel bactericides is facilitated.