Abstract
Biclustering has been very relevant within the field of gene expression data analysis. In fact, its main thrust stands in its ability to identify groups of genes that behave in the same way under a subset of samples (conditions). However, the pioneering algorithms of the literature has shown some limits in terms of the quality of unveiled biclusters. In this paper, we introduce a new algorithm, called BiFCA+, for biclustering microarray data. BiFCA+ heavily relies on the mathematical background of the formal concept analysis, in order to extract the set of biclusters. In addition, the Bond correlation measure is of use to filter out the overlapping biclusters. The extensive experiments, carried out on real-life datasets, shed light on BiFCA+’s ability to identify statistically and biologically significant biclusters.
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We use a separator-free abbreviated form for the sets; e.g., \(\{I_{1}I_{2}I_{3}\}\) stands for the set of items \(\{I_{1}, I_{2}, I_{3}\}\).
This may be either monotone increasing, monotone decreasing, up–down or down–up, etc.
Available at https://github.com/mehdi-kaytoue/trimax.
Available at http://arep.med.harvard.edu/biclustering/.
Available at http://www.tik.ethz.ch/sop/bimax/.
Available at http://arep.med.harvard.edu/biclustering/.
The human B-cell lymphoma dataset version that we have does not contain the names of genes to perform other tests.
Available at http://llama.mshri.on.ca/funcassociate/
Available at http://db.yeastgenome.org/cgi-bin/GO/goTermFinder
The adjusted significance scores assess genes in each bicluster, which indicates how well they match with the different GO categories.
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Houari, A., Ayadi, W. & Ben Yahia, S. A new FCA-based method for identifying biclusters in gene expression data. Int. J. Mach. Learn. & Cyber. 9, 1879–1893 (2018). https://doi.org/10.1007/s13042-018-0794-9
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DOI: https://doi.org/10.1007/s13042-018-0794-9