Recent advances in proteomic and transcriptomic technologies resulted in the accumulation of vast amount of high-throughput data that span multiple biological processes and characteristics in different organisms. Much of the data come in the form of interaction networks and mRNA expression arrays. An important task in systems biology is functional modules discovery where the goal is to uncover well-connected sub-networks (modules). These discovered modules help to unravel the underlying mechanisms of the observed biological processes. While most of the existing module discovery methods use only the interaction data, in this work we propose, CLARM, which discovers biological modules by incorporating gene profiles data with protein-protein interaction networks. We demonstrate the effectiveness of CLARM on Yeast and Human interaction datasets, and gene expression and molecular function profiles. Experiments on these real datasets show that the CLARM approach is competitive to well established functional module discovery methods.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We would like to thank the anonymous reviewers for their comments and suggestions. This publication was made possible by NIH grant number P20RR016471 from the INBRE program of the National Center for Research Resources.
ASJC Scopus subject areas
- Computational Mathematics
- Molecular Biology