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 :
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