Cart (Loading....) | Create Account
Close category search window
 

Inferring Adaptive Regulation Thresholds and Association Rules from Gene Expression Data through Combinatorial Optimization Learning

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Ponzoni, I. ; Univ. Nacional del Sur, Bahi''a Blanca ; Azuaje, F.J. ; Augusto, J.C. ; Glass, D.H.

There is a need to design computational methods to support the prediction of gene regulatory networks (GRNs). Such models should offer both biologically meaningful and computationally accurate predictions which, in combination with other techniques, may improve large-scale integrative studies. This paper presents a new machine-learning method for the prediction of putative regulatory associations from expression data which exhibit properties never or only partially addressed by other techniques recently published. The method was tested on a Saccharomyces cerevisiae gene expression data set. The results were statistically validated and compared with the relationships inferred by two machine-learning approaches to GRN prediction. Furthermore, the resulting predictions were assessed using domain knowledge. The proposed algorithm may be able to accurately predict relevant biological associations between genes. One of the most relevant features of this new method is the prediction of adaptive regulation thresholds for the discretization of gene expression values, which is required prior to the rule association learning process. Moreover, an important advantage consists of its low computational cost to infer association rules. The proposed system may significantly support exploratory large-scale studies of automated identification of potentially relevant gene expression associations.

Published in:

Computational Biology and Bioinformatics, IEEE/ACM Transactions on  (Volume:4 ,  Issue: 4 )

Date of Publication:

Oct.-Dec. 2007

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.