Bioinformatics with Soft Computing

Abstract—Soft computing is gradually opening up several possi-bilities in bioinformatics, especially by generating low-cost, low-precision (approximate), good solutions. In this paper, we sur-vey the role of different soft computing paradigms, like fuzzy sets (FSs), artificial neural networks (ANNs), evolutionary com-putation, rough sets (RSes), and support vector machines (SVMs), in this direction. The major pattern-recognition and data-mining tasks considered here are clustering, classification, feature selec-tion, and rule generation. Genomic sequence, protein structure, gene expression microarrays, and gene regulatory networks are some of the application areas described. Since the work entails pro-cessing huge amounts of incomplete or ambiguous biological data, we can utilize the learning ability of neural networks for adapt-ing, uncertainty handling capacity of FSs and RSes for modeling ambiguity, searching potential of genetic algorithms for efficiently traversing large search spaces, and the generalization capability of SVMs for minimizing errors. Index Terms—Artificial neural networks (ANNs), biological data mining, fuzzy sets (FSs), gene expression microarray, genetic algo-rithms (GAs), proteins, rough sets (RSes), support vector machines (SVMs). I