Année de publication
2017
Type
Journal
Description
Procedia Computer Science Volume 112, Pages 278-287
Résumé
A majority of existing biclustering algorithms for microarrays data focus only on extracting biclusters with positive correlations of genes. Nevertheless, biological studies show that a group of biologically significant genes may exhibit negative correlations. In this paper, we propose a new biclustering algorithm, called NBic-ARM (Negative Biclusters using Association Rule Mining). Based on Generic Association Rules, our algorithm identifies negatively-correlated genes. To assess NBic-ARM’s performance, we carried out exhaustive experiments on three real-life datasets. Our results prove NBic-ARM’s ability to identify statistically and biologically significant biclusters.
BibTeX
@article{HOUARI2017278,
author = {Amina Houari and Wassim Ayadi and Sadok Ben Yahia}
title = {Mining Negative Correlation Biclusters from Gene Expression Data using Generic Association Rules},
journal = {Procedia Computer Science},
volume = {112},
pages = {278-287},
year = {2017},
note = {Knowledge-Based and Intelligent Information \& Engineering Systems: Proceedings of the 21st International Conference, KES-20176-8 September 2017, Marseille, France},
issn = {1877-0509},
doi = {https://doi.org/10.1016/j.procs.2017.08.262},
url = {https://www.sciencedirect.com/science/article/pii/S187705091731671X}}
Lien
Wassim Ayadi