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47 results about "Gene interaction" patented technology

Gene interactions can result in the alteration or suppression of a phenotype. This can occur when an organism inherits two different dominant genes, for example, resulting in incomplete dominance.

Protein fragment complementation assays for high-throughput and high-content screening

The present invention provides protein fragment complementation assays for drug discovery, in particular to identify compounds that activate or inhibit cellular pathways. Based on the selection of an interacting protein pair combined with an appropriate PCA reporter, the assays may be run in high-throughput or high-content mode and may be used in automated screening of libraries of compounds. The interacting pair may be selected by cDNA library screening; by gene-by-gene interaction mapping; or by prior knowledge of a pathway. Fluorescent and luminescent assays can be constructed using the methods provided herein. The selection of suitable PCA reporters for high-throughput or high-content (high-context) assay formats is described for a diversity of reporters, with particular detail provided for examples of monomeric enzymes and fluorescent proteins. Methods are described for constructing such assays for one or more steps in a biochemical pathway; testing the effects of compounds from combinatorial, natural product, peptide, antibody, nucleic acid or other diverse libraries on the protein or pathway(s) of interest; and using the results of the screening to identify specific compounds that activate or inhibit the protein or pathway(s) of interest. Single-color and multi-color assays are disclosed. Further disclosed are universal expression vectors with cassettes that allow the rapid construction of assays for a large and diverse number of gene / reporter combinations. The development of such assays is shown to be straightforward, providing for a broad, flexible and biologically relevant platform for drug discovery.
Owner:ODYSSEY THERA INC

Analytic method for predicting body weight increase caused by treating schizophrenia through second-generation antipsychotic based on polygene combination interactive effect

The invention discloses an analysis method for predicting body weight increase caused by treating schizophrenia through a second-generation antipsychotic based on the polygene combination interactiveeffect. The method comprises the steps that a sample is prepared for collecting peripheral blood of a patient with body weight increase caused by treating schizophrenia through the second-generation antipsychotic, a hypotonic salt fractionation method is used for extracting a genome DNA sample; a MODLI-TOF flight mass spectrum detecting method is used for performing genetic typing of a 5-HT2CR gene, a histamine 1 receptor gene, an oxytocin gene, an NPY / R gene, a Leptin gene, an adiponectin gene, an FGF21 gene and a FGF23 gene; data analysis and extraction are performed, instrument original data is aligned, data with no noise interference for statistic analysis is obtained, a generalized multi-factor dimension reduction method is used for performing the quantitative trait gene-gene interaction effect, crossed verification is further applied, and the gene interaction effect is used for predicting body weight increase. By adopting the generalized multi-factor dimension reduction method, the continuous ending variable is processed, covariance is introduced, and the method has the advantages of greatly enlarging the application range and greatly increasing the prediction accuracy.
Owner:SECOND AFFILIATED HOSPITAL OF XINJIANG MEDICAL UNIV

Microarrays to screen regulatory genes

Microarray technology allows the multiple parallel processing of information generated from matrices of huge numbers of loci on a solid substrate, which is useful in the gathering of gene signatures defining specific biological states. An approach has been developed to facilitate this process wherein genes of the same regulatory modality are selected. The transcriptional regulation of these genes is related to the same control element. Primers specific for the regulatory genes are selected, based on minimum cross-reactivity with other genes, using known gene data banks. PCR products of selected regions of known genes either binding to this sequence or whose expression is dependent on this binding, as well as genes interacting with the regulatable genes and control genes, referred to as “amplicons” or “gene cDNA fragments” of between about 450 and 1000 nucleotide bases in length, are obtained from a total RNA pool. These amplicons are arrayed on a nylon membrane or other appropriate microchip susbstrate, which is then used as a regulatory gene-specific microarray that is hybridized with sample. Sample will typically be the mRNA obtained from cells associated with a particular state (examples include age or exposure to conditions such as outspace, low gravity), disease (such as cancer or an infection), or disorder (such as a genetic defect or trauma). The transcriptionally regulated profile of regulatory gene-related genes specific to a given cultured cell sample is then determined using a software based analysis of the amount of hybridization which is detected. This information is useful in determining drug targets, markers associated with the disease state (either the presence or absence, or the extent of the disease), or the response of the disease state to drugs or other treatments.
Owner:LOUISVILLE RES FOUND UNIV OF

Method and system for predicting interaction between miRNA and gene based on multi-relational graph convolutional network

PendingCN114093422ARich forecasting meansFully capture the structureNeural architecturesSequence analysisInformation networksHeterogeneous network
The invention discloses a method and a system for predicting interaction between miRNA and a gene based on a multi-relational graph convolutional network. The method comprises: constructing a heterogeneous information network of miRNA and genes, and learning network topology features of nodes by using a multi-relational graph convolutional network based on the heterogeneous network; meanwhile, capturing effective characteristics of the gene sequence by using a recurrent neural network; and finally, combining network topology characteristics with sequence characteristics, and calculating an association prediction score of the miRNA-gene pair by using the obtained miRNA and gene embedding. According to the method, manual feature construction is not needed in the implementation process, representation learning is combined, the advantages of the multi-relation graph convolutional network are fully utilized, effective gene sequence information is mined, and feature representation of miRNA and gene nodes is better captured. Experimental results show that the MRMTI is superior to other comparison methods in the aspect of association prediction of miRNA and genes, and has good prediction performance.
Owner:HUNAN UNIV
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