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 |
|---|---|
| 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 May 13 |
| 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) |
|---|---|
| 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 |
|---|---|
| Country | Italy |
| City | Torino |
| Period | 11/4/27 → 11/4/29 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Protein complex discovery from protein interaction network with high false-positive rate. / Yeu, Yunku; Ahn, Jaegyoon; Yoon, Youngmi; Park, Sanghyun.
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 9th European Conference, EvoBIO 2011, Proceedings. 2011. p. 177-182 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6623 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Protein complex discovery from protein interaction network with high false-positive rate
AU - Yeu, Yunku
AU - Ahn, Jaegyoon
AU - Yoon, Youngmi
AU - Park, Sanghyun
PY - 2011/5/13
Y1 - 2011/5/13
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79955778052&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79955778052&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-20389-3_19
DO - 10.1007/978-3-642-20389-3_19
M3 - Conference contribution
AN - SCOPUS:79955778052
SN - 9783642203886
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 177
EP - 182
BT - Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 9th European Conference, EvoBIO 2011, Proceedings
ER -