9 results about "Protein protein interaction network" patented technology

The invention relates to a novel chemical cross-linking agent with mass spectrum fragmentable trehalose disaccharide as a skeleton structure and a preparation method thereof. The cross-linking agent disclosed by the invention has the following characteristics: 1) trehalose disaccharide is used as a skeleton structure, so that the cross-linking agent has excellent biocompatibility; 2) the trehalose skeleton has a pair of symmetrical mass spectrum fragmentable glucosidic bonds, so that a cross-linked peptide fragment can be simplified into a conventional peptide fragment modified by a cross-linking agent fragment; 3) the enrichment of the cross-linked peptide fragment can be realized by the trehalose skeleton under the condition of not adding an enrichment handle; and 4) active groups of the cross-linking agent comprise but not limited to a plurality of reactive groups such as succinamide ester, diaziridine, phenylsulfonyl fluoride, hydrazide group, amino group, hydroxylamine group and the like, and chemical cross-linking of a plurality of amino acids except lysine is realized. The trehalose cross-linking agent disclosed by the invention is applied to the field of proteomics, and provides technical support for realizing large-scale analysis of a protein complex in a complex sample, spatial structure analysis of protein and a protein-protein interaction network.

Methods and systems for generating drug repurposing predictions for a disease caused by a pathogen, such as a novel pathogen, are provided. A multi-modal system includes a protein-protein interaction network (PPI), a graph neural network (GNN), a diffusion module, a proximity module, and an aggregation module. The GNN is configured to predict new edges between candidate drug nodes and disease nodes in an embedded representation of the PPI to produce a decoded embedding space. The diffusion module is configured to determine a proximity distance for pairs of nodes in the PPI, and the proximity module is configured to determine a proximity distance for pairs of nodes in the PPI, each pair comprising a pathogen-protein node and a drug-protein node. A ranked list of candidate drugs predicted to be effective in treatment of the disease based on candidate drug lists generated by the other modules is generated by the aggregation module.

The invention provides a network pharmacology-based large model generated traditional Chinese medicine prescription curative effect evaluation method and system, and the method comprises the steps: employing a machine learning model to generate candidate prescriptions, and obtaining all known active components of each traditional Chinese medicine in the prescriptions; predicting human body protein targets corresponding to the patient based on the known active components, and gathering all the human body protein targets to obtain a prescription target set P; obtaining human body protein targets related to the corresponding diseases, and gathering all the human body protein targets corresponding to the diseases to obtain a disease target set D; constructing a protein-protein interaction network; inputting the prescription target point set P and the disease target point set D into a protein-protein interaction network, and constructing a connected sub-network; calculating curative effect indexes of the connected sub-networks; and optimizing the prescription based on the curative effect index to complete the curative effect evaluation of the traditional Chinese medicine prescription. According to the method, calculation and experiments are closely combined, the blindness and the cost of experimental verification are remarkably reduced through a calculation priority strategy, and the research and development efficiency is improved.

Machine learning models to predict, based on genomic data and gene expression data associated with a particular patient cancer tissue sample, one or more characteristics for that particular patient cancer tissue sample. The machine learning models are trained using respective feature sets representing one or more topological characteristics of respective protein-protein interaction, "PPI", networks representing models of different cancers, and that indicate reaction pathways associated with the protein interactions.

The invention belongs to the technical field of biological medicine, and discloses a method for analyzing an action target of artemisinin for treating renal clear cell carcinoma based on network pharmacology and a molecular docking technology, and the method comprises the following steps: S1, screening the action target of an artemisinin active component; s2, identifying candidate disease targets; s3, acquiring an intersection target point; s4, constructing a protein-protein interaction network and performing network topology analysis; s5, core target biological function and molecular action signal path analysis and visualization; s6, constructing an artemisinin-renal clear cell carcinoma-target spot-pathway network diagram; s7, exploring an action mechanism of artemisinin for improving the renal clear cell carcinoma based on a molecular docking technology; s8, carrying out survival analysis on the core target spot based on the TCGA database, and drawing a Kaplan-Meier curve; and S9, observing the influence of artemisinin and derivatives thereof on the activity of renal clear cell carcinoma 786-O cells by adopting a CCK-8 method. Network pharmacology and molecular docking technologies are applied, multiple omics data are integrated from the perspective of system biology, a drug-target-disease network is constructed, the interaction between artemisinin and renal clear cell carcinoma related targets is deeply analyzed, and the action mechanism of artemisinin on the aspects of genes, proteins, signal channels and the like is comprehensively revealed.

The application provides serum biomarkers for diagnosing and evaluating the activity of aortitis and application, and relates to the field of disease activity evaluation. The serum biomarkers are serum proteins: HP and ORM1. HP and ORM1 are determined as two new serum biomarkers through comprehensive proteomics analysis; gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis are carried out using a DAVID database; a protein-protein interaction network is constructed using Metascape. A random forest model is trained using a caret package, and a nomogram is visualized using an rms package. Serum samples are obtained, and the serum levels of HP and ORM1 are determined using a commercial ELISA kit; they are significantly related to disease activity, and are involved in inflammation / immune processes in aortitis, so the markers have the potential to serve as an auxiliary tool for monitoring the activity of aortitis.

A computer-based system and computer-implemented method are employed for network medicine. The network medicine employs representations of interactions of biological components. The computer-implemented method employs a machine-readable representation of a transformer weighted protein-protein interactome (PPI) network to perform a computer-informed network medicine task. The machine-readable representation of the transformer weighted PPI network includes machine-readable links weighted based on biological context information extracted from a transformer-based model. The machine-readable links are interposed between machine-readable representations of proteins in the transformer weighted PPI network. The biological context information extracted from the transformer-based model is based on machine-readable genetic data. The computer-implemented method outputs a result of the computer-informed network medicine task performed.

This invention discloses a DNA damage repair scoring method based on an adaptive heterogeneous graph attention network, comprising: S1. Acquiring genomic data and constructing a multidimensional omics feature matrix including gene expression scores, copy number variation scores, methylation scores, and somatic mutation scores; acquiring protein-protein interaction network data and constructing an adjacency matrix; S2. Capturing topological dependence features of the protein-protein interaction network through graph attention perception branches, screening high signal-to-noise ratio omics features through gating feature screening branches, and extracting deep semantic features through a multilayer perceptron; S3. Feature fusion to obtain high-dimensional fused features; S4. Outputting a predicted score. This invention solves the problem of functional directionality ambiguity, effectively preserves and enhances weakly expressed but functionally active gene signals, truncates false positive error edges, and overcomes the problem of "homogeneous redundancy".

The invention discloses application of a traditional Chinese medicine compound Shenhua tablet in treatment of diabetic nephropathy and a research method of an action mechanism of the traditional Chinese medicine compound Shenhua tablet in treatment of diabetic nephropathy. The research method comprises the following steps: screening active ingredients and action targets of the Shenhua tablet; obtaining related targets of the diabetic nephropathy from the target library; screening a drug-disease intersection target spot; drawing a protein-protein interaction network; performing gene ontology function enrichment analysis and Beijing gene and genome encyclopedia pathway analysis; carrying out molecular docking simulation and visual analysis; performing grouping intervention on the experimental animals; and carrying out kidney tissue pathological evaluation, real-time fluorescent quantitative PCR detection, western blot analysis and immunofluorescence microscopic imaging. The core active ingredients and the main action mechanism of the Shenhua tablet for treating diabetic nephropathy are systematically researched for the first time, an experimental basis and an innovative view angle are provided for clinical application and targeted therapy of the Shenhua tablet, and meanwhile, a theoretical basis is laid for treating the disease by combining traditional Chinese medicine and western medicine.