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• W2023188413 abstract "Biologically a gene or a sample could participate in multiple biological pathways, and only few genes are concurrently involved in a cellular process under some specific experimental conditions. Hence, identification of a subset of genes showing similar regulations under subsets of condition in microarray data has become an important research issue. Many investigators develop bi-clustering methods to attack this problem. In this study, we adopt fuzzy co-clustering concept and design a procedure to iteratively extract bi-clusters with co-expressed gene patterns (here the entire proposed process is called a modified fuzzy co-clustering (MFCC) approach). We have applied synthetic data and compared our MFCC's performance with four well-known state-of-the-art methods. Here we have not only shown that our MFCC approach can successfully extract each designed bi-clusters in the synthetic data sets, but also have demonstrated the better performance by our MFCC approach." @default.
• W2023188413 created "2016-06-24" @default.
• W2023188413 creator A5021101384 @default.
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• W2023188413 date "2014-07-01" @default.
• W2023188413 modified "2023-09-24" @default.
• W2023188413 title "A modified fuzzy co-clustering (MFCC) approach for microarray data analysis" @default.
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• W2023188413 doi "https://doi.org/10.1109/fuzz-ieee.2014.6891707" @default.
• W2023188413 hasPublicationYear "2014" @default.
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