NDLI: Inferring gene-gene associations from Quantitative Association Rules

Content Provider	IEEE Xplore Digital Library
Author	Martinez-Ballesteros, M. Nepomuceno-Chamorro, I. Riquelme, J.C.
Copyright Year	2011
Description	Author affiliation: Department of Computer Science, University of Seville, Seville, Spain (Martinez-Ballesteros, M.; Nepomuceno-Chamorro, I.; Riquelme, J.C.)
Abstract	The microarray technique is able to monitor the change in concentration of RNA in thousands of genes simultaneously. The interest in this technique has grown exponentially in recent years and the difficulties in analyzing data from such experiments, which are characterized by the high number of genes to be analyzed in relation to the low number of experiments or samples available. Microarray experiments are generating datasets that can help in reconstructing gene networks. One of the most important problems in network reconstruction is finding, for each gene in the network, which genes can affect it and how. Association Rules are an approach of unsupervised learning to relate attributes to each other. In this work we use Quantitative Association Rules in order to define interrelations between genes. These rules work with intervals on the attributes, without discretizing the data before and they are generated by a multi-objective evolutionary algorithm. In most cases the extracted rules confirm the existing knowledge about cell-cycle gene expression, while hitherto unknown relationships can be treated as new hypotheses.
Starting Page	1241
Ending Page	1246
File Size	221810
Page Count	6
File Format	PDF
ISBN	9781457716768
ISSN	21647151
e-ISBN	9781457716768
DOI	10.1109/ISDA.2011.6121829
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Radiation detectors gene networks Evolutionary computation evolutionary algorithms Association rules Proposals Data mining Intelligent systems quantitative association rules
Content Type	Text
Resource Type	Article

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