Discovering Low Overlapping Biclusters in Gene Expression Data Through Generic Association Rules

Autor:	Amina Houari, Wassim Ayadi, Sadok Ben Yahia
Rok vydání:	2015
Předmět:	Biclustering Set (abstract data type) Association rule learning Originality Computer science media_common.quotation_subject Dna microarray data Data mining computer.software_genre computer media_common Task (project management)
Zdroj:	Model and Data Engineering ISBN: 9783319237800 MEDI
DOI:	10.1007/978-3-319-23781-7_12
Popis:	Biclustering is a thriving and of paramount task in many biomedical applications. Indeed, the biclusters aim, among-others, the discovery of unveiling principles of cellular organizations and functions, to cite but a few. In this paper, we introduce a new algorithm called, BiARM, that aims to efficiently extract the most meaningful, low overlapping biclusters. The main originality of our algorithm stands in the fact that it relies on the extraction of generic association rules. The reduced set of association rules faithfully mimics relationships between sets of genes, proteins, or other cell members and gives important information for the analysis of diseases. The effectiveness of our method has been proved through extensive carried out experiments on real-life DNA microarrayi?źdata.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7322d9ff956f570a6f6f8985d93c854d https://doi.org/10.1007/978-3-319-23781-7_12 Zobrazit plný text záznamu