NDLI: Inferring Protein Modulation from Gene Expression Data Using Conditional Mutual Information

Content Provider	PubMed Central
Author	Giorgi, Federico M. Lopez, Gonzalo Woo, Jung H. Bisikirska, Brygida Califano, Andrea Bansal, Mukesh
Editor	Rattray, Magnus
Copyright Year	2014
Abstract	Systematic, high-throughput dissection of causal post-translational regulatory dependencies, on a genome wide basis, is still one of the great challenges of biology. Due to its complexity, however, only a handful of computational algorithms have been developed for this task. Here we present CINDy (Conditional Inference of Network Dynamics), a novel algorithm for the genome-wide, context specific inference of regulatory dependencies between signaling protein and transcription factor activity, from gene expression data. The algorithm uses a novel adaptive partitioning methodology to accurately estimate the full Condition Mutual Information (CMI) between a transcription factor and its targets, given the expression of a signaling protein. We show that CMI analysis is optimally suited to dissecting post-translational dependencies. Indeed, when tested against a gold standard dataset of experimentally validated protein-protein interactions in signal transduction networks, CINDy significantly outperforms previous methods, both in terms of sensitivity and precision.
Related Links	http://dx.doi.org/10.1371/journal.pone.0109569
Starting Page	109569
File Format	PDF
ISSN	19326203
e-ISSN	19326203
Journal	PLoS ONE
Issue Number	10
Volume Number	9
Language	English
Publisher	Public Library of Science
Publisher Date	2014-10-01
Access Restriction	Open
Rights Holder	Public Library of Science
Subject Keyword	Biochemistry, Genetics and Molecular Biology(all) Agricultural and Biological Sciences(all) Medicine(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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