Soft Computing Methods in Bioinformatics: A Comprehensive Review

Applications of genomic and proteomic, epigenetic, pharmacogenomics, and systems biology have shown increased a lot, resulting in an explosion in the amount of highly dimensional and complicated data being generated. The data of bioinformatics fields are always with high-dimension and small samples. Genome-wide investigations generate in large numbers of data and there is a need for soft computing methods (SCMs) such as artificial neural networks, fuzzy systems, evolutionary algorithms, metaheuristic and swarm intelligence algorithms, statistical model algorithms etc. that can deal with this amount of data. The use of soft computing methods has been increased to a variety of bioinformatics applications. It is used to inquire the underlying mechanisms and interactions between biological molecules in a lot of diseases, and it is a main tool in any biological (or biomarker) discovery process. The aim of this article is to introduce soft computing methods for bioinformatics. These methods present supervised or unsupervised classification, clustering and statistical or stochastic heuristics models for knowledge discovery. In this article, the current problems and the prospects of SCMs in the application of bioinformatics is also discussed.