Get Computational Intelligence in Bioinformatics (IEEE Press PDF

By Gary B. Fogel

ISBN-10: 0470105267

ISBN-13: 9780470105269

ISBN-10: 0470199083

ISBN-13: 9780470199084

Combining biology, laptop technological know-how, arithmetic, and data, the sphere of bioinformatics has turn into a sizzling new self-discipline with profound affects on all elements of biology and commercial software. Now, Computational Intelligence in Bioinformatics deals an advent to the subject, masking the main appropriate and well known CI equipment, whereas additionally encouraging the implementation of those ways to readers' study.

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Vol. 23, pp. 187–202. , R. Tibshirani, and J. Friedman (2003). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York. Haykin, S. (1999). Neural Networks: A Comprehensive Foundation, 2nd ed. Prentice Hall, Upper Saddle River, NJ. Healy, M. and T. Caudell (1997). “Acquiring rule sets as a product of learning in the logical neural architecture LAPART,” IEEE Trans. , Vol. 8, pp. 461–474. , T. Caudell, and S. Smith (1993). “A neural architecture for pattern sequence verification through inferencing,” IEEE Trans.

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. Acad. , USA, Vol. 99, pp. 6562–6566. Anagnostopoulos, G. C. (2001). Novel Approaches in Adaptive Resonance Theory for Machine Learning, Doctoral Thesis, University of Central Florida, Orlando, Florida.

The method was verified on three representative data sets and produced better performance than conventional approaches. Langdon and Buxton (2004) also proposed a rule discovering method by using GP. Various arithmetic functions were employed to construct a rule and the size of rule was limited at 5 or 9. The experiment on central nervous systems showed the usefulness of mining genes from genetic programming. 3 GENE SELECTION WITH SPECIATED GENETIC ALGORITHM The first case study in this chapter focuses on gene selection using the speciated GA (sGA).

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Computational Intelligence in Bioinformatics (IEEE Press Series on Computational Intelligence) by Gary B. Fogel

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