44 results about "Gene module" patented technology

Dysfunctional or exhausted T cells arise in chronic diseases including chronic viral infections and cancer, and express high levels of co-inhibitory receptors. Therapeutic blockade of these receptors has clinical efficacy in the treatment of cancer. While co-inhibitory receptors are co-expressed, the triggers that induce them and the transcriptional regulators that drive their co-expression have not been identified. The immunoregulatory cytokine IL-27 induces a gene module in T cells that includes several known co-inhibitory receptors (Tim-3, Lag-3, and TIGIT). The present invention provides a novel immunoregulatory network as well as novel cell surface molecules that have an inhibitory function in the tumor microenvironment. The present invention further provides the novel discovery that the transcription factors Prdm1 and c-Maf cooperatively regulate the expression of the co-inhibitory receptor module. This critical molecular circuit underlies the co-expression of co-inhibitory receptors in dysfunctional T cells and identifies novel regulators of T cell dysfunction.

The invention discloses a survival prediction method and system based on image genomics. The method comprises the following steps: acquiring image data of tumor patients and lifetime data and RNA data of each patient, and establishing a data set; segmenting a tumor area of each patient from the image data; inputting the image data of each patient into a neural network to extract image features and cluster the image features to obtain a plurality of image modules; obtaining a gene module of each patient by using the RNA data; performing screening according to the correlation between the gene modules and the image modules, and selecting a plurality of gene modules and image modules which are strongly correlated; performing pathway enrichment on genes in the selected gene module to obtain a gene pathway related to the image module; calculating a gene set variation analysis score of the gene pathway, and retaining the gene pathway having strong correlation with the image module; and carrying out survival prediction by using the retained image features. According to the invention, the biological interpretability in the aspect of survival prediction can be improved, and the generalization ability of deep learning can be improved at the same time.

The invention discloses a gene module analysis method. The gene module analysis method comprises the following steps: inputting a gene phenotype double-layer network, a gene function similarity network and a known disease gene set s0 related to a disease phenotype; increasing the connection relationship between genes and phenotypes in the gene phenotype double-layer network; taking genes which arein edge connection with disease genes in s0 and are not in s0 in the gene phenotypic double-layer network as candidate genes; calculating and selecting a candidate gene with the maximum sum of semantic similarity, topological similarity and phenotypic relevance, and adding the candidate gene into s0; and when the expanded candidate gene set does not significantly enrich the GO ontology function annotation and the biological pathway gene related to the disease phenotype at the same time and express the differential gene in the disease phenotype sample and the normal sample any more, recordingthe current algebra as m, and outputting m-1 as candidate genes expanded in the first generation s0 and known disease genes connected with the edges of the expanded candidate genes.

The invention discloses a lung cancer prognosis comprehensive prediction model and a construction method and device. The method comprises the following steps: collecting original gene expression data and corresponding clinical survival data of a lung cancer sample and conducting data preprocessing and standardization, thereby acquiring a gene expression matrix; obtaining the types of immune cells in the tumor and calculating the relative ratio of the various types of immune cells; screening out parameters for constructing a prognosis prediction model from the obtained immune cell types and obtaining corresponding regression coefficients, wherein the parameters are multiple immune cell types; based on the screened parameters, calculating an immune score according to the relative ratio and the corresponding regression coefficient; identifying a gene co-expression module, searching for a gene module cooperatively expressed in the lung cancer sample, and determining a gene module related to prognosis; and constructing a prognosis comprehensive prediction model. According to the invention, immune scores, clinical information and gene co-expression module characteristics are integrated to construct a comprehensive prediction model to predict prognosis of lung cancer patients.

The invention is about the gene health card and the gene recorder. The gene recorder includes the power supply, the operation control keyboard, the memorizer, the monitor, the CPU, the encryption/deciphering module, the fingerprint module, the blood type module, the character figure module, the gene module and the faucet which matches to the read-write interface of the gene health card. The fingerprint module and the encryption/deciphering module identify the personal identity then change it into the electronic number such as the DNA detecting results(the personal DNA encoding and the atlas) and memory it to transfer to other databank or the personal gene health card. The gene health card records the personal stature, the weight, the age, the sex, the head portrait, the blood type, the fingerprint, the DNA encoding and the part DNA atlas, so it is benefit for analyzing the personal health.

The invention provides a ceRNA competition model identification method and device, electronic equipment and a storage medium, and relates to the field of gene identification. The ceRNA competition model identification method comprises the steps of obtaining an RNA1 gene module and an RNA2 gene module according to the expression matrix of the RNA1 and the expression matrix of the RNA2, wherein theRNA1 and the RNA2 are miRNA target genes; regulating relation data, a miRNA expression matrix, an RNA1 gene module and an RNA2 gene module according to the prior miRNA-target gene, and performing identification for obtaining the ceRNA competition module which satisfies a condition, wherein the ceRNA competition module comprises an inner competition module and an outer competition module. Because the inner and outer competition conditions of the ceRNA are considered, the inner and outer competition strength of the ceRNA and the influence of the miRNA to the inner and outer competition strengthsof the ceRNA are measured in a module level. Therefore, the method provided by the invention can accurately identify the ceRNA competition module.

The invention discloses a multiple myeloma molecular subtype and application. The product provided by the invention comprises a substance for obtaining or detecting 97 gene expressions in a multiple myeloma cancer patient to be detected. The product further comprises equipment for operating a multiple myeloma Bayes classifier. In the invention, a gene module (MCL1-M for short) co-expressed with anMCL1 gene is identified, and the gene module is applied to divide multiple myeloma into two main subtypes, namely an MCL-M-High subtype and an MCL-M-Low subtype. The two subtypes have prognosis and genetics characteristics which are significantly different.

The invention provides an escherichia coli recombinant bacterium for denovo synthesis of a vitamin B12, and a construction method and application of the escherichia coli recombinant bacterium. Specifically, escherichia coli serves as a starting strain, the engineering bacterium containing an HBA gene module, an HBAD gene module, a CBAD gene module and a Cbi gene module is constructed, the engineering bacterium efficiently and rapidly produces the B12 (the yield is at least 50 times higher than that of the prior art), and simple compounds can even be used as raw materials to conduct denovo synthesis on the vitamin B12.

The invention discloses a method for establishing a human brain gene expression space-time norm. The method comprises the following steps: collecting brain space-time original data to obtain a gene standardized expression data set; constructing a two-dimensional space-time framework of a brain region and a development stage, and enabling all gene standardized expression data to correspond to the constructed space-time framework according to the brain region and development stage information to obtain a single-gene space-time expression mode; analyzing the obtained single-gene space-time expression matrix data by adopting a gene co-expression network analysis method to obtain a plurality of gene modules, and averaging the gene expression quantities of the gene modules to obtain a gene module space-time expression mode; analyzing the normal human nerve cell expression data, and performing the enrichment analysis of characteristic genes of nerve cells and the obtained gene modules to obtain a cell expression mode of the gene module. According to the method, all reference data sets are finally presented in the same two-dimensional space-time framework, so that the data sets from different sources are integrated, and researchers can intuitively supplement and compare the content of the space-time framework.

The invention belongs to the technical field of high-throughput sequencing data analysis, and particularly relates to a method for screening transcription factors related to postharvest softening of grape fruits. According to the invention, hypotaurine (HT) and hydrogen peroxide (H2O2) treatment is carried out on harvested 'Kyoho' grapes, through transcriptome sequencing and weighted gene co-expression network analysis (WGCNA), correlation analysis is carried out on differential expression genes obtained through transcriptome analysis and pectin content change, a differential expression gene module closely related to the pectin content change is screened out, and the specific value of the differential expression genes and the specific value of the pectin content change are obtained. And excavating differential expression transcription factors in the softening process of the picked grape fruits. According to the method, the softening character related transcription factors can be effectively screened, and a theoretical reference is provided for researching the softening occurrence mechanism of the picked grape fruits.

The invention discloses a method for efficiently converting a gene module and line for a rice leaf sheath protoplast, and relates to a conversion method of the rice leaf sheath protoplast. The methodcomprises the steps of adding an MMg solution to the rice leaf sheath protoplast to obtain a rice leaf sheath protoplast suspension; adding plasmid and a polyethylene glycol-calcium solution of whichthe percentage by mass is 40% to the rice leaf sheath protoplast suspension, performing uniform mixing, and performing standing; and adding a W5 solution, performing uniform mixing, performing centrifugation, removing supernatant, performing suspension once again and precipitation on the W5 solution, performing centrifugation, removing supernatant, adding carbenicillin, performing uniform mixing,and performing culturing to complete transformation of the protoplast. According to the conversion method of the rice leaf sheath protoplast, preparation, plasmid concentration, conversion time and the like of the PEG solution are adjusted, so that the conversion efficiency is improved to 80%, intelligent reformation devices can exert functions in 80% of plant cells, available data can be providedfor research on functions of the devices at the initial stage, and convenience is provided for research on functions of the intelligent reformation devices for crops at the later stage.

The invention provides an efficient artificial rhizosphere combined nitrogen fixation system constructed according to synthetic biology theories and methods. Two brand-new functional modules are respectively and manually designed: a'Neb + amtR 'ammonium secreting gene module is constructed in a nitrogen-fixing microorganism chassis, a'Ham' nitrogen efficient utilization module is constructed in non-leguminous crop chassis of rice, corn, wheat and the like, and functional coupling of the two artificial modules is realized through inoculation technologies such as seed coating and the like at crop rhizosphere. Compared with a combined nitrogen fixation system in the prior art, the artificial rhizosphere combined nitrogen fixation system has higher biological nitrogen fixation efficiency and plant growth promoting effect.

The invention discloses a biosynthetic gene cluster of a maduramicin compound and application of the biosynthetic gene cluster. The biosynthetic gene cluster of the maduramicin compound comprises an I-type linear polyketone synthetase gene module, a glycosyl synthesis related gene module, a modified gene module and a regulatory gene module, and the I-type linear polyketone synthetase gene module comprises a madA1 gene, a madA2 gene, a madA3 gene, a madA4 gene, a madA5 gene, a madA6 gene, a madA7 gene, a madA8 gene, a madA9 gene, a madA10 gene and a madA11 gene; the glycosyl synthesis related gene module comprises a madG1 gene, a madG2 gene, a madG3 gene, a madG4 gene, a madG5 gene, a madG6 gene, a madG7 gene and a madG8 gene; the modified gene module comprises a madO1 gene, a madO2 gene, amadI gene, a madE gene, a madP1 gene, a madP2 gene, a madM1 gene, a madM2 gene, a madG9 gene, a madG10 gene and a madTE gene; and the regulatory gene module comprises a madR1 gene, a madR2 gene, a madR3 gene and a madR4 gene.

The invention discloses a gene network reasoning method based on modular identification. The method takes the expression information of n genes as the training set, and each gene has m samples; uses the ICA-FDR algorithm to identify the gene modules and divides the n genes into different gene modules; the regulation relationship within the gene module The algorithm based on gradient boosting tree is used for inference, and the regulatory relationship between gene modules is inferred by the algorithm based on sparse regression, and the correlation score of each gene pair is obtained; for each gene module and inter-module reasoning The obtained correlation scores were respectively normalized and merged, and sorted in descending order to obtain the final gene regulatory network. The invention provides a seamless fusion framework for gene module identification and gene network reasoning, improves the accuracy of gene module identification, and increases the interpretability of gene regulation network functions.