Journal of Biomedical Informatics
Application of fuzzy subtractive clustering for enzymes classification
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Model selection approaches for non-linear system identification: a review
International Journal of Systems Science
Multi-sample test-based clustering for fuzzy random variables
International Journal of Approximate Reasoning
A new method for design and reduction of neuro-fuzzy classification systems
IEEE Transactions on Neural Networks
Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset
Computers and Operations Research
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Efficient gene selection with rough sets from gene expression data
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Gene selection and cancer classification: a rough sets based approach
Transactions on rough sets XII
Genetic Networks and Soft Computing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A position-velocity cooperative intelligent controller based on the biological neuroendocrine system
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Isolating top-k dense regions with filtration of sparse background
Pattern Recognition Letters
Inference system using softcomputing and mixed data applied in metabolic pathway datamining
International Journal of Data Mining and Bioinformatics
Review article: Computational intelligence techniques in bioinformatics
Computational Biology and Chemistry
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Soft computing is gradually opening up several possibilities in bioinformatics, especially by generating low-cost, low-precision (approximate), good solutions. In this paper, we survey the role of different soft computing paradigms, like fuzzy sets (FSs), artificial neural networks (ANNs), evolutionary computation, 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 selection, 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 processing huge amounts of incomplete or ambiguous biological data, we can utilize the learning ability of neural networks for adapting, 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