Combining gene expression profiles and protein-protein interactions for identifying functional modules

Dingding Wang, Mitsunori Ogihara, Erliang Zeng, Tao Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Identifying functional modules from protein-protein interaction networks is an important and challenging task. This paper presents a new approach called PPIBM which is designed to integrate gene expression data analysis and clustering of protein-protein interactions. The proposed approach relies on a Bayesian model which uses as its base protein-protein interactions given as part of input. The proposed method is evaluated with standard measures and its performance is compared with the state-of-the-art network analysis methods. Experimental results on both real-world data and synthetic data demonstrate the effectiveness of the proposed approach.

Original languageEnglish (US)
Title of host publicationProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Pages114-119
Number of pages6
DOIs
StatePublished - Dec 1 2012
Event11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012 - Boca Raton, FL, United States
Duration: Dec 12 2012Dec 15 2012

Publication series

NameProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Volume1

Other

Other11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
CountryUnited States
CityBoca Raton, FL
Period12/12/1212/15/12

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Education

Fingerprint Dive into the research topics of 'Combining gene expression profiles and protein-protein interactions for identifying functional modules'. Together they form a unique fingerprint.

Cite this