Inferring protein modulation from gene expression data using conditional mutual information.
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 (C...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2014-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC4196905?pdf=render |
id |
doaj-c75a1555b9944ab3a9bf6044883048e3 |
---|---|
record_format |
Article |
spelling |
doaj-c75a1555b9944ab3a9bf6044883048e32020-11-25T01:56:05ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01910e10956910.1371/journal.pone.0109569Inferring protein modulation from gene expression data using conditional mutual information.Federico M GiorgiGonzalo LopezJung H WooBrygida BisikirskaAndrea CalifanoMukesh BansalSystematic, 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.http://europepmc.org/articles/PMC4196905?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Federico M Giorgi Gonzalo Lopez Jung H Woo Brygida Bisikirska Andrea Califano Mukesh Bansal |
spellingShingle |
Federico M Giorgi Gonzalo Lopez Jung H Woo Brygida Bisikirska Andrea Califano Mukesh Bansal Inferring protein modulation from gene expression data using conditional mutual information. PLoS ONE |
author_facet |
Federico M Giorgi Gonzalo Lopez Jung H Woo Brygida Bisikirska Andrea Califano Mukesh Bansal |
author_sort |
Federico M Giorgi |
title |
Inferring protein modulation from gene expression data using conditional mutual information. |
title_short |
Inferring protein modulation from gene expression data using conditional mutual information. |
title_full |
Inferring protein modulation from gene expression data using conditional mutual information. |
title_fullStr |
Inferring protein modulation from gene expression data using conditional mutual information. |
title_full_unstemmed |
Inferring protein modulation from gene expression data using conditional mutual information. |
title_sort |
inferring protein modulation from gene expression data using conditional mutual information. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2014-01-01 |
description |
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. |
url |
http://europepmc.org/articles/PMC4196905?pdf=render |
work_keys_str_mv |
AT federicomgiorgi inferringproteinmodulationfromgeneexpressiondatausingconditionalmutualinformation AT gonzalolopez inferringproteinmodulationfromgeneexpressiondatausingconditionalmutualinformation AT junghwoo inferringproteinmodulationfromgeneexpressiondatausingconditionalmutualinformation AT brygidabisikirska inferringproteinmodulationfromgeneexpressiondatausingconditionalmutualinformation AT andreacalifano inferringproteinmodulationfromgeneexpressiondatausingconditionalmutualinformation AT mukeshbansal inferringproteinmodulationfromgeneexpressiondatausingconditionalmutualinformation |
_version_ |
1724981631859556352 |