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How information spreads through society; robustness and fragility of food webs and financial markets; algorithms for the World Wide Web; friend prediction in online social networks; identification of functional modules in biological networks; disease outbreak detection.
This study is a proposal toward the identification of functional modules in biological networks.
The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization.
To assess the performance of Walktrap to find genomic modules (Walktrap-GM), we evaluate our results against other tools recently developed to discover disease modules in biological networks.
Leukemia cancer data was selected to show the effectiveness of our method to detect phenotype-related gene modules in biological networks.
While detection and analysis of functional modules in biological systems have received great attention in recent years, we still lack a complete understanding of how such modules emerge.
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Unfortunately, it is not always clear what constitutes a module in biological systems, nor how modules can be interconnected.
Discovery of modules (subnetworks) in biological networks helps isolate systems with disease-related properties and reduces interactome complexity.
Functional modules in single biological networks (e.g., the protein-protein interaction network), have been the focus of recent studies.
In this study, we propose a novel method to learn contextual gene set interaction networks that can represent maps of functional modules in target biological systems with statistical interactions between gene sets, and identify condition/subtype-specificity of inferred interactions.
While DENSE is an effective and efficient algorithm to identify the functional modules in a biological network, it can only be applied to a single network at a time.
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