Protein complex discovery from protein interaction network with high false-positive rate

Yunku Yeu; Jaegyoon Ahn; Youngmi Yoon; Sanghyun Park

doi:10.1007/978-3-642-20389-3_19

Protein complex discovery from protein interaction network with high false-positive rate

Yunku Yeu, Jaegyoon Ahn, Youngmi Yoon, Sanghyun Park

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

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	https://doi.org/10.1007/978-3-642-20389-3_19
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)
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/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)

Access to Document

10.1007/978-3-642-20389-3_19

Cite this

Yeu, Y., Ahn, J., Yoon, Y., & Park, S. (2011). Protein complex discovery from protein interaction network with high false-positive rate. In Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 9th European Conference, EvoBIO 2011, Proceedings (pp. 177-182). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6623 LNCS). https://doi.org/10.1007/978-3-642-20389-3_19

Yeu, Yunku ; Ahn, Jaegyoon ; Yoon, Youngmi et al. / Protein complex discovery from protein interaction network with high false-positive rate. Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 9th European Conference, EvoBIO 2011, Proceedings. 2011. pp. 177-182 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

author = "Yunku Yeu and Jaegyoon Ahn and Youngmi Yoon and Sanghyun Park",

note = "Funding Information: This work was supported by National Research Foundation of Korea funded by the Korean Government under Grant (No. 2010-0003965).; 9th European Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics, EvoBIO 2011 ; Conference date: 27-04-2011 Through 29-04-2011",

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Yeu, Y, Ahn, J, Yoon, Y & Park, S 2011, Protein complex discovery from protein interaction network with high false-positive rate. in Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 9th European Conference, EvoBIO 2011, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6623 LNCS, pp. 177-182, 9th European Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics, EvoBIO 2011, Torino, Italy, 11/4/27. https://doi.org/10.1007/978-3-642-20389-3_19

Protein complex discovery from protein interaction network with high false-positive rate. / Yeu, Yunku; Ahn, Jaegyoon; Yoon, Youngmi et al.

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

N1 - Funding Information: This work was supported by National Research Foundation of Korea funded by the Korean Government under Grant (No. 2010-0003965).

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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.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 9th European Conference, EvoBIO 2011, Proceedings

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Yeu Y, Ahn J, Yoon Y, Park S. Protein complex discovery from protein interaction network with high false-positive rate. In 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)). doi: 10.1007/978-3-642-20389-3_19

Protein complex discovery from protein interaction network with high false-positive rate

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