Molecular Pathway Identification via Research Graph Scoring
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Solution Overview
Problem
Current methods for identifying perturbed molecular pathways in diseases are limited by accuracy, complexity, and high computational requirements, failing to provide a complete understanding of disease pathophysiology.
Innovation Solution
A system and method using a research graph to extract gene relationships from a pre-curated database, map them onto a molecular pathway connectivity graph, identify sub-networks, assign gene scores, and determine perturbed pathways based on interconnectivity, focusing on the most relevant pathways for disease or drug remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pathway prioritization approaches (Bayesian, Signaling pathway impact analysis, Gene Graph Enrichment Analysis, Topology-based pathway analysis, Over-representation Analysis) are used, then pathway identification can be performed, but accuracy is limited and computational requirements are high
Solution Approach 1:
The patent segments the complex pathway analysis problem into distinct functional modules: a graph construction module that builds molecular interaction networks, a graph query module that executes efficient queries on the constructed graphs, and a pathway enrichment module that performs statistical analysis. This segmentation allows each module to be optimized independently, improving overall accuracy while reducing computational overhead compared to monolithic conventional approaches.
Solution Approach 2:
The patent performs preliminary actions by pre-construction and caching of molecular interaction graphs from omics data before actual pathway analysis is needed. The graph construction module pre-processes and stores molecular interaction networks in an optimized format, so that subsequent pathway queries can be executed efficiently without repeating computationally intensive data processing steps.
2Loss of information
If comprehensive gene and pathway data are analyzed to understand disease pathophysiology, then complete understanding is achieved, but system complexity increases
Solution Approach 1:
The patent introduces molecular interaction graphs as an intermediary structure between raw omics data and pathway analysis results. These graphs serve as a mediator that organizes complex gene-protein-pathway relationships into a structured format, enabling comprehensive disease understanding while simplifying the analysis process through graph-based queries and enrichment algorithms.
Solution Approach 2:
The molecular interaction graphs serve multiple functions simultaneously: they store molecular interaction data, enable efficient pathway queries, support enrichment analysis, and facilitate disease mechanism exploration. This multi-functionality reduces system complexity by eliminating the need for separate specialized tools for each analytical task.
3Measurement precision
If traditional experimental approaches are used to identify perturbed pathways, then results can be obtained, but time and resources required are excessive
Solution Approach 1:
The patent replaces traditional wet-lab experimental approaches with computational methods based on molecular interaction graphs and graph query algorithms. Instead of performing time-consuming experimental validations to identify perturbed pathways, the system uses in silico analysis of omics data through graph-based enrichment analysis, dramatically reducing time and resource requirements while maintaining or improving accuracy.
Data Source
AI summary
A method for identifying molecular pathways perturbed under influence of a drug or a disease includes extracting a relationship dataset related to genes and molecular pathways associated with the genes, from a pre-curated database. The method further includes mapping the relationship dataset onto a research graph. The method further includes identifying, sub-networks within the research graph, and assigning a gene score to each gene in the identified sub-networks, based on whether a gene is neutral, dysregulated, or associated with a disease-specific organ. The method further includes determining a perturbed molecular pathway for genes within the identified sub-networks based on the gene score and a molecular pathway interconnectivity within the research graph. The perturbed molecular pathway for genes has a highest association with a pathophysiology of the disease or the drug response as compared to other molecular pathways associated with the genes in the research graph.