Abstract
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.
Original language | English |
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Title of host publication | Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 9th European Conference, EvoBIO 2011, Proceedings |
Pages | 177-182 |
Number of pages | 6 |
DOIs | |
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 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 6623 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 9th European Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics, EvoBIO 2011 |
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Country/Territory | Italy |
City | Torino |
Period | 11/4/27 → 11/4/29 |
Bibliographical note
Funding 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)