Granular Fuzzy Possibilistic C-Means Clustering approach to DNA microarray problem

Deoxyribonucleic acid (DNA) microarray is an important technology, which supports a simultaneous measurement of thousands of genes for biological analysis. With the rapid development of the gene expression data characterized by uncertainty and being of high dimensionality, there is a genuine need fo...

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Bibliographic Details
Published in:Knowledge-based systems 2017-10, Vol.133, p.53-65
Main Authors: Truong, Hung Quoc, Ngo, Long Thanh, Pedrycz, Witold
Format: Article
Language:English
Subjects:
Arrays
Clustering
Comparative analysis
Computation
Data analysis
Deoxyribonucleic acid
DNA
DNA analysis
Feature selection
Fuzzy clustering
Fuzzy logic
Fuzzy Possibilistic C-Means Clustering
Gene expression
Gene expression data
Granular computing
Microarray technology
Outliers (statistics)
Preprocessing
Uncertainty
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Summary:Deoxyribonucleic acid (DNA) microarray is an important technology, which supports a simultaneous measurement of thousands of genes for biological analysis. With the rapid development of the gene expression data characterized by uncertainty and being of high dimensionality, there is a genuine need for advanced processing techniques. With this regard, Fuzzy Possibilistic C-Means Clustering (FPCM) and Granular Computing (GrC) are introduced with the aim to solve problems of feature selection and outlier detection. In this study, by taking advantage of the FPCM and GrC, an Advanced Fuzzy Possibilistic C-Means Clustering based on Granular Computing (GrFPCM) is proposed to select features as a preprocessing phase for clustering problems while the developed granular space is used to cope with uncertainty. Experiments were completed for various gene expression datasets and a comparative analysis is reported.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2017.06.019