A new FCA-based method for identifying biclusters in gene expression data
| Autor: | Wassim Ayadi, Sadok Ben Yahia, Amina Houari |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Systems biology 0206 medical engineering Computational intelligence 02 engineering and technology computer.software_genre Filter (higher-order function) Field (computer science) Set (abstract data type) Biclustering Artificial Intelligence Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Formal concept analysis 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Data mining computer 020602 bioinformatics Software |
| Zdroj: | International Journal of Machine Learning and Cybernetics. 9:1879-1893 |
| ISSN: | 1868-808X 1868-8071 |
| DOI: | 10.1007/s13042-018-0794-9 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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