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

• Published in: BioData mining Vol. 8; no. 1; p. 38
• Main Authors: Saber, Haifa Ben, Elloumi, Mourad
• Format: Journal Article
• Language: English
• Published: London BioMed Central 30.11.2015; BioMed Central Ltd; Springer Nature B.V
• Subjects: 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
• DOI: 10.1186/s13040-015-0070-4

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 .