Finding protein complexes and their functions is essential work for understanding biological process. However, one of the difficulties in inferring protein complexes from protein-protein interaction(PPI) network originates from the fact that protein interactions suffer from high false positive rate. We propose a complex finding algorithm which is not strongly dependent on topological traits of the protein interaction network. Our method exploits a new measure, GECSS(Gene Expression Condition Set Similarity) which considers mRNA expression data for a set of PPI. The complexes we found exhibit a higher match with reference complexes than the existing methods. Also we found several novel protein complexes, which are significantly enriched on Gene Ontology database.
|Title of host publication||Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 9th European Conference, EvoBIO 2011, Proceedings|
|Number of pages||6|
|Publication status||Published - 2011|
|Event||9th European Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics, EvoBIO 2011 - Torino, Italy|
Duration: 2011 Apr 27 → 2011 Apr 29
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||9th European Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics, EvoBIO 2011|
|Period||11/4/27 → 11/4/29|
Bibliographical noteFunding Information:
This work was supported by National Research Foundation of Korea funded by the Korean Government under Grant (No. 2010-0003965).
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)