DocumentCode
1164386
Title
Computational functional genomics
Author
Liang, Mike P. ; Troyanskaya, Olga G. ; Laederach, Alain ; Brutlag, Douglas L. ; Altman, Russ B.
Author_Institution
Dept. of Genetics, Stanford Univ. Medical Center, CA, USA
Volume
21
Issue
6
fYear
2004
Firstpage
62
Lastpage
69
Abstract
The exponential growth of the publicly available data has transformed biology into an information rich science that provides new and interesting applications for the machine learning community. In this article, the author presents some specific examples regarding the possibility of representing biological data in a machine-learning framework as well as the contributions these representations impart to both the prediction and discovery of the biological function. The paper also illustrates the proper feature selection critical to the success of the of a particular computational functional genomics approach.
Keywords
cellular biophysics; genetics; learning (artificial intelligence); molecular biophysics; proteins; biological data; cellular function identification; computational functional genomics approach; feature selection; machine-learning framework; molecular function identification; protein sequence data; Bioinformatics; Biological processes; Biological system modeling; Biological systems; Biology computing; Genomics; Large-scale systems; Machine learning algorithms; Organisms; Sequences;
fLanguage
English
Journal_Title
Signal Processing Magazine, IEEE
Publisher
ieee
ISSN
1053-5888
Type
jour
DOI
10.1109/MSP.2004.1359143
Filename
1359143
Link To Document