NDLI: A Self-Tuning Genetic Algorithm with Applications in Biomarker Discovery

Content Provider	IEEE Xplore Digital Library
Author	Popovic, D. Moschopoulos, C. Sakai, R. Sifrim, A. Aerts, J. Moreau, Y. De Moor, B.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. Eng. (ESAT), KU Leuven, Leuven, Belgium (Popovic, D.; Moschopoulos, C.; Sakai, R.; Sifrim, A.; Aerts, J.; Moreau, Y.; De Moor, B.)
Abstract	Recent developments in the field of-omics technologies brought great potential for conducting biomedical research in very efficient manner, but also raised a plethora of new computational challenges to be addressed. Extremely high dimensionality accompanied with poor signal-to-noise ratio and small sample size of data resulting from high-throughput experiments pose previously unprecedented problem, creating an increasing demand for innovative analytical strategies. In this work we propose an island model-based genetic algorithm for multivariate feature selection in the context of-omics data, which accommodates to a particular classification scenario via dynamic tuning of its parameters. We demonstrate it on two publicly available data sets containing gene expression profiles corresponding to the two distinct biomedical questions. We show that the algorithm consistently outperforms two additional feature selection schemes across data sets, regardless to which method is used in the subsequent classification step.
Starting Page	233
Ending Page	238
File Size	553267
Page Count	6
File Format	PDF
ISBN	9781479944354
ISSN	23729198
DOI	10.1109/CBMS.2014.10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithms Biological cells Genetics Optimization Classification algorithms Biomedical measurement Sociology biomarker discovery genetic algorithm self-tuning island model feature selection
Content Type	Text
Resource Type	Article

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