Biomolecular Network Key Module Identification via Multi-Metric Ranking
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Solution Overview
Problem
Current methods for identifying core modules or core nodes in complex biomolecular networks neglect the topological structure and lack quantitative analysis, making it difficult to effectively understand and target key components in disease mechanisms and drug development.
Innovation Solution
A method that identifies core modules or core nodes by constructing weighted or unweighted module interaction networks and using multiple metrics for measuring node importance, such as Degree centrality, Closeness centrality, and PageRank, to rank nodes and modules based on their impact on network structure and information transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple metrics are used to measure node importance, then identification accuracy of core modules is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex identification process into distinct phases: module detection phase (using MCODE or similar algorithms to identify candidate modules), module importance evaluation phase (calculating betweenness centrality and other metrics for each module), and core module identification phase (selecting modules that meet predefined criteria). This segmentation allows each phase to be optimized independently, managing computational complexity while maintaining identification accuracy.
Solution Approach 2:
The patent employs multiple parameters (betweenness centrality, module size, node degree, clustering coefficient) to evaluate module importance rather than relying on a single parameter. By changing and combining multiple parameters, the method achieves more accurate identification of core modules while the parameters can be weighted and adjusted to balance accuracy requirements against computational resources available.
2Loss of information
If topological structure is considered in module identification, then biological function understanding is improved, but method complexity increases
Solution Approach 1:
The patent performs preliminary module detection using established algorithms (such as MCODE, ClusterONE, or other community detection algorithms) before conducting topological analysis. This preliminary action identifies candidate modules based on dense connectivity patterns, and then topological metrics (betweenness centrality, bridge detection) are applied to these pre-identified modules. This approach preserves biological function information by considering topological structure while avoiding the complexity of simultaneously performing module detection and topological analysis from scratch.
Solution Approach 2:
The patent uses module betweenness centrality as an intermediary metric that bridges the gap between topological structure and biological function. Instead of directly analyzing complex topological relationships, the method calculates betweenness centrality for each module (based on the number of shortest paths passing through the module), which serves as a quantitative mediator reflecting the module's importance in information flow and biological function, thereby simplifying the analysis while preserving functional understanding.
3Reliability
If quantitative analysis is applied to core modules, then identification reliability is improved, but data processing requirements increase
Solution Approach 1:
The patent extracts and focuses analysis on specifically identified core modules rather than performing exhaustive quantitative analysis on all modules in the network. By using preliminary filtering criteria (module size thresholds, density requirements, and topological metrics) to extract the most promising candidate modules, the method achieves high identification reliability while significantly reducing the quantity of data that requires intensive quantitative processing. Only the extracted core modules undergo detailed quantitative validation.
Solution Approach 2:
The patent applies quantitative analysis selectively to certain modules rather than uniformly to all modules. By calculating detailed quantitative metrics (such as precise betweenness centrality, information flow measurements, and functional enrichment analyses) only for modules that meet preliminary screening criteria, the method achieves sufficient identification reliability for the most important core modules while avoiding the excessive data processing requirements that would result from applying the same level of quantitative analysis to every module in the network.
Data Source
AI summary
A method for identifying a key module and a key node of a biomolecular complex network by fusing multiple methods on the basis of a topological structure of the biomolecular complex network which may be such as a protein-protein interaction network, a gene expression regulation network, a biological metabolism network, an epigenetic network, a phenotypic network or a signaling network, comprising the following main steps: according to the biomolecular network and a module division for the network, comprehensively and quantitatively identifying the key module and key node on the basis of the topological structure by various measuring methods from multi-angle.