Abstract
Formal Concept Analysis has been widely applied to identify differently expressed genes among microarray data. Top-K Formal Concepts are identified as efficient in generating most important Formal Concepts. To the best of our knowledge, no currently available algorithm is able to perform this challenging task. Therefore, we introduce Top-BicMiner, a new method for mining biclusters from gene expression data through Top-k Formal Concepts. It performs the extraction of the sets of both positive and negative correlations biclusters. Top-BicMiner relies on Formal concept analysis as well as a specific discretization method. Extensive experiments, carried out on real-life datasets, shed light on Top-BicMiner’s ability to identify statistically and biologically significant biclusters.
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Notes
- 1.
The extraction of the formal concepts is carried out through the invocation of the efficient LCM algorithm [23].
- 2.
In fact, coherent formal concepts having an intersection size above or equal to the given threshold \(\alpha 1\) belong to the same bicluster, while those with an intersection value below it, do not.
.
- 3.
Available at https://github.com/mehdi-kaytoue/trimax.
- 4.
Available at http://arep.med.harvard.edu/biclustering/.
- 5.
Available at http://arep.med.harvard.edu/biclustering/.
- 6.
Available at http://llama.mshri.on.ca/funcassociate/.
- 7.
The best biclusters have an adjusted p-value less than 0.001%.
- 8.
Available at https://www.yeastgenome.org/goTermFinder.
- 9.
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Houari, A., Ben Yahia, S. (2021). Top-K Formal Concepts for Identifying Positively and Negatively Correlated Biclusters. In: Attiogbé, C., Ben Yahia, S. (eds) Model and Data Engineering. MEDI 2021. Lecture Notes in Computer Science(), vol 12732. Springer, Cham. https://doi.org/10.1007/978-3-030-78428-7_13
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