A systems biology approach to solving the puzzle of unknown genomic gene-function association using grid-ready SVM committee machines

Tsung Lu Michael Lee, Jung Hsien Chiang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Genomic researchers face the common challenge of deriving the functions of genes and proteins from high-throughput data. Experimental validation of protein function is costly and time-consuming. With the increased effectiveness of computational intelligence approaches, researchers aim to target the problem with in silico prediction of protein interactions and functions. We propose a systems biology approach that consists of machine-learning and visualization intelligence and aims to predict protein-protein interactions and enhance protein function annotation. Our machine-learning intelligence, SVM committee machines, is compatible with grid computing and large-scale data analysis. In this paper, we not only elucidate the computational power of protein interactions prediction, but also aim to emphasize the interpretation of protein function annotation through protein interaction network analysis.

Original languageEnglish
Article number6331720
Pages (from-to)46-54
Number of pages9
JournalIEEE Computational Intelligence Magazine
Volume7
Issue number4
DOIs
Publication statusPublished - 2012 Oct 30

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'A systems biology approach to solving the puzzle of unknown genomic gene-function association using grid-ready SVM committee machines'. Together they form a unique fingerprint.

  • Cite this