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Bioinformatics 2009 25(12):i128-i136; doi:10.1093/bioinformatics/btp192
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

KELLER: estimating time-varying interactions between genes

Le Song , Mladen Kolar and Eric P. Xing *

School of Computer Science, Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Gene regulatory networks underlying temporal processes, such as the cell cycle or the life cycle of an organism, can exhibit significant topological changes to facilitate the underlying dynamic regulatory functions. Thus, it is essential to develop methods that capture the temporal evolution of the regulatory networks. These methods will be an enabling first step for studying the driving forces underlying the dynamic gene regulation circuitry and predicting the future network structures in response to internal and external stimuli.

Results: We introduce a kernel-reweighted logistic regression method (KELLER) for reverse engineering the dynamic interactions between genes based on their time series of expression values. We apply the proposed method to estimate the latent sequence of temporal rewiring networks of 588 genes involved in the developmental process during the life cycle of Drosophila melanogaster. Our results offer the first glimpse into the temporal evolution of gene networks in a living organism during its full developmental course. Our results also show that many genes exhibit distinctive functions at different stages along the developmental cycle.

Availability: Source codes and relevant data will be made available at http://www.sailing.cs.cmu.edu/keller

Contact: epxing{at}cs.cmu.edu


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