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International Journal of Machine Learning and Cybernetics

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07.03.2018 | Original Article

A new FCA-based method for identifying biclusters in gene expression data

verfasst von: Amina Houari, Wassim Ayadi, Sadok Ben Yahia

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 11/2018

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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.

Vorheriger Artikel Connections between two-universe rough sets and formal concepts

Nächster Artikel Relation granulation and algebraic structure based on concept lattice in complex information systems

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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/

http://geneontology.org/

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.

http://geneontology.org/

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Titel: A new FCA-based method for identifying biclusters in gene expression data
verfasst von: Amina Houari
Wassim Ayadi
Sadok Ben Yahia
Publikationsdatum: 07.03.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal of Machine Learning and Cybernetics / Ausgabe 11/2018
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI: https://doi.org/10.1007/s13042-018-0794-9

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