Computational functional genomics
Liang, M.P.
Troyanskaya, O.G.
Laederach, A.
Brutlag, D.L.
Altman, R.B.
Dept. of Genetics, Stanford Univ. Medical Center, CA, USA;
This paper appears in: Signal Processing Magazine, IEEE
Publication Date: Nov. 2004
Volume: 21,
Issue: 6
On page(s): 62- 69
ISSN: 1053-5888
INSPEC Accession Number: 8197488
Digital Object Identifier: 10.1109/MSP.2004.1359143
Current Version Published: 2004-11-22
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.
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