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

Saber HB, Elloumi M - BioData Min (2015)

Bottom Line: The construction of biologically significant groups of biclusters for large microarray data is still a problem that requires a continuous work.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.

View Article: PubMed Central - PubMed

Affiliation: Latice laboratory, ENSIT, Tunis Time université, Tunis, Tunisia.

ABSTRACT
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.

No MeSH data available.


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

Saber HB, Elloumi M - BioData Min (2015)

© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4666179&req=5

Bottom Line: The construction of biologically significant groups of biclusters for large microarray data is still a problem that requires a continuous work.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.

View Article: PubMed Central - PubMed

Affiliation: Latice laboratory, ENSIT, Tunis Time université, Tunis, Tunisia.

ABSTRACT
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.

No MeSH data available.