Analysis of gene microarray data in a soft computing framework
Applied Soft Computing
Evolving random boolean networks with genetic algorithms for regulatory networks reconstruction
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A visual analytics framework for cluster analysis of DNA microarray data
Expert Systems with Applications: An International Journal
An evolutionary computational model applied to cluster analysis of DNA microarray data
Expert Systems with Applications: An International Journal
Evolving fisher kernels for biological sequence classification
Evolutionary Computation
RNA Secondary Structure Prediction Using Soft Computing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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This paper provides an overview of the application of evolutionary algorithms in certain bioinformatics tasks. Different tasks such as gene sequence analysis, gene mapping, deoxyribonucleic acid (DNA) fragment assembly, gene finding, microarray analysis, gene regulatory network analysis, phylogenetic trees, structure prediction and analysis of DNA, ribonucleic acid and protein, and molecular docking with ligand design are, first of all, described along with their basic features. The relevance of using evolutionary algorithms to these problems is then mentioned. These are followed by different approaches, along with their merits, for addressing some of the aforesaid tasks. Finally, some limitations of the current research activity are provided. An extensive bibliography is included