Title :
Fuzzy biclustering for DNA microarray data analysis
Author :
Han, Lixin ; Yan, Hong
Author_Institution :
Dept. of Comput. Sci. & Eng., Hohai Univ., Nanjing
Abstract :
Fuzzy biclustering analysis is a useful tool for identifying relevant subsets of microarray data. This paper proposes a fuzzy biclustering clustering method for microarray data analysis. The method employs a combination of the Nelder-Mead and min-max algorithm to construct hierarchically structured biclustering. The method can automatically identify the groups of genes that show similar expression patterns under a specific subset of the samples.
Keywords :
DNA; biology computing; data analysis; fuzzy set theory; minimax techniques; pattern clustering; DNA microarray data analysis; Nelder-Mead algorithm; fuzzy biclustering clustering method; hierarchically structured biclustering; min-max algorithm; Algorithm design and analysis; Clustering algorithms; Clustering methods; DNA; Data analysis; Gene expression; Genetic algorithms; Iterative algorithms; Monitoring; Sorting;
Conference_Titel :
Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1818-3
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2008.4630513
