A novel biclustering algorithm of binary microarray data: BiBinCons and BiBinAlter

The biclustering of microarray data has been the subject of a large research. No one of the existing biclustering algorithms is perfect. The construction of biologically significant groups of biclusters for large microarray data is still a problem that requires a continuous work. Biological validati...

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Veröffentlicht in:BioData mining Jg. 8; H. 1; S. 38
Hauptverfasser: Saber, Haifa Ben, Elloumi, Mourad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 30.11.2015
BioMed Central Ltd
Springer Nature B.V
Schlagworte:
Algorithms
Analysis
Bioinformatics
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Data Mining and Knowledge Discovery
Deoxyribonucleic acid
DNA
Genomics
Information management
Life Sciences
Microarray data analysis
Biclustering
Algorithm
Evaluation function
ISSN:1756-0381, 1756-0381
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Zusammenfassung:The biclustering of microarray data has been the subject of a large research. No one of the existing biclustering algorithms is perfect. The construction of biologically significant groups of biclusters for large microarray data is still a problem that requires a continuous work. Biological validation of biclusters of microarray data is one of the most important open issues. So far, there are no general guidelines in the literature on how to validate biologically extracted biclusters. In this paper, we develop two biclustering algorithms of binary microarray data, adopting the Iterative Row and Column Clustering Combination (IRCCC) approach, called BiBinCons and BiBinAlter . However, the BiBinAlter algorithm is an improvement of BiBinCons . On the other hand, BiBinAlter differs from BiBinCons by the use of the EvalStab and IndHomog evaluation functions in addition to the CroBin one (Bioinformatics 20:1993–2003, 2004). BiBinAlter can extracts biclusters of good quality with better p-values .
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ISSN:1756-0381
1756-0381
DOI:10.1186/s13040-015-0070-4