Computational functional genomics

Mike P. Liang, Olga G. Troyanskaya, Alain Laederach, Douglas L. Brutlag, Russ B. Altman

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Biology has emerged as an information rich science providing many new and interesting applications for the machine learning community. Biological data is challenging to analyze because it is inherently noisy and biased. A review is given to illustrate how both effective representation of these data and proper feature selection are critical to the success of a particular computational functional genomics approach. The types of computational challenges that arise in functional genomics and current methodologies that are employed are also outlined.

Original languageEnglish (US)
Pages (from-to)62-69
Number of pages8
JournalIEEE Signal Processing Magazine
Volume21
Issue number6
DOIs
StatePublished - Nov 2004

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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    Liang, M. P., Troyanskaya, O. G., Laederach, A., Brutlag, D. L., & Altman, R. B. (2004). Computational functional genomics. IEEE Signal Processing Magazine, 21(6), 62-69. https://doi.org/10.1109/MSP.2004.1359143