Classifying protein complexes from candidate subgraphs using fuzzy machine learning model

Bo Xu, Hongfei Lin, Zhihao Yang, Kavishwar B. Wagholikar, Hongfang Liu

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

1 Scopus citations

Abstract

Many computational methods have been applied to identify protein complexes from experimentally obtained protein-protein interaction (PPI) networks. Because of the presence of unreliable interactions in PPI networks, multi-functionality of proteins, and complex connectivity of the PPI network, the task is very challenging. In this study, we tackle the presence of unreliable interactions in protein complex using Genetic-Algorithm Fuzzy Naïve Bayes (GAFNB) which takes unreliability into consideration. Many existing methods can provide lots of candidate subgraphs. So we focused on how to classify the protein complexes from the subgraphs by considering the fuzzy attribute of PPI. We experimented with two datasets of size 10,371 and 986, each containing 493 positive protein complexes from MIPS and TAP-MS datasets. We compared the performance of GAFNB with Naïve Bayes (NB). Results show that GAFNB performed better which indicates that a fuzzy model is more suitable when unreliability is present. It is necessary to consider the unreliability in identifying protein complexes.

Original languageEnglish (US)
Title of host publicationProceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2012
Pages640-647
Number of pages8
DOIs
StatePublished - Dec 1 2012
Event2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2012 - Philadelphia, PA, United States
Duration: Oct 4 2012Oct 7 2012

Publication series

NameProceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2012

Other

Other2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2012
CountryUnited States
CityPhiladelphia, PA
Period10/4/1210/7/12

Keywords

  • Machine Learning
  • Naïve Bayes
  • Protein complexes

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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