By Gary B. Fogel
Combining biology, desktop technological know-how, arithmetic, and records, the sector of bioinformatics has develop into a scorching new self-discipline with profound affects on all features of biology and business software. Now, Computational Intelligence in Bioinformatics deals an advent to the subject, overlaying the main suitable and well known CI tools, whereas additionally encouraging the implementation of those ways to readers' examine.
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Extra info for Computational Intelligence in Bioinformatics (IEEE Press Series on Computational Intelligence)
1, the most effective performance was achieved when PSO-FIXEW was used, where PSO achieves the expected accuracy within 39 iterations on average, except for 2 runs that did not converge. 9 for 16 out of 20 converging cases. PSO-RADW did not perform well in this problem and was more dependent on its initialization. 8, and set both c1 and c2 to 2. 8, and adjusted the value of ε, which controlled the amount of misclassiﬁcation allowed in the training phase. The parameters of ssEAM were determined based on a simple selection procedure in which the data set was randomly divided into training and validation sets.
Tran, X. Yu, J. Powell, L. Yang, G. Marti, T. Moore, J. , L. Lu, D. Lewis, R. Tibshirani, G. Sherlock, W. Chan, T. Greiner, D. Weisenburger, J. Armitage, R. Warnke, R. Levy, W. Wilson, M. Grever, J. Byrd, D. Bostein, P. Brown, and L. Staudt (2000). “Distinct types of diffuse large B-cell lymphoma identiﬁed by gene expression proﬁling,” Nature, Vol. 403, pp. 503–511. Ambroise, C. and G. McLachlan (2002). “Selection bias in gene extraction on the basis of microarray gene-expression data,” Proc. Natl.
Ando and Iba (2004) compared the simple GA, artiﬁcial immune system, correlation metric, and random selection for feature selection, and used k-nearest-neighbor, support vector machines, and naïve Bayes classiﬁer as base classiﬁers. On a set of benchmark data, the combination of naïve Bayes classiﬁers and artiﬁcial immune system yielded the best results. Liu et al. (2005) applied the GAs to multiclass cancer classiﬁcation. 2 Evolutionary Approach to the Problem 27 paired support vector machines.