Multiconstrained gene clustering based on generalized projections

Background: Gene clustering for annotating gene functions is one of the fundamental issues in bioinformatics. The best clustering solution is often regularized by multiple constraints such as gene expressions, Gene Ontology (GO) annotations and gene network structures. How to integrate multiple pieces of constraints for an optimal clustering solution still remains an unsolved problem. Results: We propose a novel multiconstrained gene clustering (MGC) method within the generalized projection onto convex sets (POCS) framework used widely in image reconstruction. Each constraint is formulated as a corresponding set. The generalized projector iteratively projects the clustering solution onto these sets in order to find a consistent solution included in the intersection set that satisfies all constraints. Compared with previous MGC methods, POCS can integrate multiple constraints from different nature without distorting the original constraints. To evaluate the clustering solution, we also...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Jia Zeng, Shanfeng Zhu, Alan Wee-Chung Liew, Hong Yan
• Keywords: BMCBI 2010, Clustering Solutions, Gene, Gene Functions