Active module identification in intracellular networks using a memetic algorithm with a new binary decoding scheme
Research output: Contribution to journal › Article › peer-review
Colleges, School and Institutes
- PLA University of Science and Technology
- Shenzhen University, Shenzhen, China.
Background: Active modules are connected regions in biological network which show significant changes in expression over particular conditions. The identification of such modules is important since it may reveal the regulatory and signaling mechanisms that associate with a given cellular response. Results: In this paper, we propose a novel active module identification algorithm based on a memetic algorithm. We propose a novel encoding/decoding scheme to ensure the connectedness of the identified active modules. Based on the scheme, we also design and incorporate a local search operator into the memetic algorithm to improve its performance. Conclusion: The effectiveness of proposed algorithm is validated on both small and large protein interaction networks.
|Publication status||Published - 14 Mar 2017|