By Gary B. Fogel
Combining biology, laptop technological know-how, arithmetic, and data, the sector of bioinformatics has develop into a scorching new self-discipline with profound affects on all features of biology and business program. Now, Computational Intelligence in Bioinformatics deals an creation to the subject, overlaying the main suitable and renowned CI tools, whereas additionally encouraging the implementation of those tips on how to readers' examine.
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Additional resources for Computational Intelligence in Bioinformatics (IEEE Press Series on Computational Intelligence)
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Conventional encoding schemes that encode all possible features are not suitable to large-scale feature selection. 2 is used, which ﬁxes the number of features involved. The number of selected features ns can vary according to the number of all features nf, where ns is set as 25 for the work presented in this chapter. 2 Chromosome representation used in work presented in this chapter. 30 Chapter 2 Classifying Gene Expression Proﬁles with Evolutionary Computation measured by classifying training samples with neural networks that use features selected according to the chromosome representation.
Pan Copyright © 2008 the Institute of Electrical and Electronics Engineers, Inc. 21 22 Chapter 2 Classifying Gene Expression Proﬁles with Evolutionary Computation • A data set consisting of 62 samples of colon epithelial cells taken from colon cancer patients. Each sample was represented as 2000 gene expression levels, which were reduced from the original data of 6000 gene expression levels according to the conﬁdence in the measured expression levels. Forty of 62 samples were labeled as colon cancer and the remaining samples were labeled as normal.