CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2013 vol.10 Issue No.02 - March-April
Issue No.02 - March-April (2013 vol.10)
Pradipta Maji , Machine Intell. Unit, Indian Stat. Inst., Kolkata, India
Sushmita Paul , Machine Intell. Unit, Indian Stat. Inst., Kolkata, India
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.103
Gene expression data clustering is one of the important tasks of functional genomics as it provides a powerful tool for studying functional relationships of genes in a biological process. Identifying coexpressed groups of genes represents the basic challenge in gene clustering problem. In this regard, a gene clustering algorithm, termed as robust rough-fuzzy c-means, is proposed judiciously integrating the merits of rough sets and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in cluster definition, the integration of probabilistic and possibilistic memberships of fuzzy sets enables efficient handling of overlapping partitions in noisy environment. The concept of possibilistic lower bound and probabilistic boundary of a cluster, introduced in robust rough-fuzzy c-means, enables efficient selection of gene clusters. An efficient method is proposed to select initial prototypes of different gene clusters, which enables the proposed c-means algorithm to converge to an optimum or near optimum solutions and helps to discover coexpressed gene clusters. The effectiveness of the algorithm, along with a comparison with other algorithms, is demonstrated both qualitatively and quantitatively on 14 yeast microarray data sets.
Clustering algorithms, Approximation methods, Gene expression, Probabilistic logic, Prototypes, Robustness, Indexes,rough sets, Clustering algorithms, Approximation methods, Gene expression, Probabilistic logic, Prototypes, Robustness, Indexes, fuzzy sets, Microarray, gene clustering, overlapping clustering
Pradipta Maji, Sushmita Paul, "Rough-Fuzzy Clustering for Grouping Functionally Similar Genes from Microarray Data", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.10, no. 2, pp. 286-299, March-April 2013, doi:10.1109/TCBB.2012.103