Natural computing methods in bioinformatics: A survey

Abstract

Often data analysis problems in Bioinformatics concern the fusion of multisensor outputs or the fusion of multisource information, where one must integrate different kinds of biological data. Natural computing provides several possibilities in Bioinformatics, especially by presenting interesting nature-inspired methodologies for handling such complex problems. In this article we survey the role of natural computing in the domains of protein structure prediction, microarray data analysis and gene regulatory network generation. We utilize the learning ability of neural networks for adapting, uncertainty handling capacity of fuzzy sets and rough sets for modeling ambiguity, and the search potential of genetic algorithms for efficiently traversing large search spaces.