Genetic algorithms

Gene selection based on multi-class support vector machines and genetic algorithms

B. Feres de Souza and de Carvalho, A. Ponce de L., Gene selection based on multi-class support vector machines and genetic algorithms, vol. 4, pp. 599-607, 2005.

Microarrays are a new technology that allows biologists to better understand the interactions between diverse pathologic state at the gene level. However, the amount of data generated by these tools becomes problematic, even though data are supposed to be automatically analyzed (e.g., for diagnostic purposes). The issue becomes more complex when the expression data involve multiple states. We present a novel approach to the gene selection problem in multi-class gene expression-based cancer classification, which combines support vector machines and genetic algorithms.

A simple genetic algorithm for multiple sequence alignment

C. Gondro and Kinghorn, B. P., A simple genetic algorithm for multiple sequence alignment, vol. 6, pp. 964-982, 2007.

Multiple sequence alignment plays an important role in molecular sequence analysis. An alignment is the arrangement of two (pairwise alignment) or more (multiple alignment) sequences of ‘residues’ (nucleotides or amino acids) that maximizes the similarities between them. Algorithmically, the problem consists of opening and extending gaps in the sequences to maximize an objective function (measurement of similarity). A simple genetic algorithm was developed and implemented in the software MSA-GA.

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