NDLI: Rough-Fuzzy Clustering for Grouping Functionally Similar Genes from Microarray Data

Content Provider	ACM Digital Library
Author	Maji, Pradipta Paul, Sushmita
Abstract	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.
Starting Page	286
Ending Page	299
Page Count	14
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2012.103
Volume Number	10
Issue Number	2
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-03-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	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
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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