Fused Regression for Multi-source Gene Regulatory Network Inference.
Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each...
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2016-12-01
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Series: | PLoS Computational Biology |
Online Access: | https://doi.org/10.1371/journal.pcbi.1005157 |
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doaj-205cea2881814891a6de2381e22952052021-04-21T15:39:17ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582016-12-011212e100515710.1371/journal.pcbi.1005157Fused Regression for Multi-source Gene Regulatory Network Inference.Kari Y LamZachary M WestrickChristian L MüllerLionel ChristiaenRichard BonneauUnderstanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms.https://doi.org/10.1371/journal.pcbi.1005157 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kari Y Lam Zachary M Westrick Christian L Müller Lionel Christiaen Richard Bonneau |
spellingShingle |
Kari Y Lam Zachary M Westrick Christian L Müller Lionel Christiaen Richard Bonneau Fused Regression for Multi-source Gene Regulatory Network Inference. PLoS Computational Biology |
author_facet |
Kari Y Lam Zachary M Westrick Christian L Müller Lionel Christiaen Richard Bonneau |
author_sort |
Kari Y Lam |
title |
Fused Regression for Multi-source Gene Regulatory Network Inference. |
title_short |
Fused Regression for Multi-source Gene Regulatory Network Inference. |
title_full |
Fused Regression for Multi-source Gene Regulatory Network Inference. |
title_fullStr |
Fused Regression for Multi-source Gene Regulatory Network Inference. |
title_full_unstemmed |
Fused Regression for Multi-source Gene Regulatory Network Inference. |
title_sort |
fused regression for multi-source gene regulatory network inference. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS Computational Biology |
issn |
1553-734X 1553-7358 |
publishDate |
2016-12-01 |
description |
Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms. |
url |
https://doi.org/10.1371/journal.pcbi.1005157 |
work_keys_str_mv |
AT kariylam fusedregressionformultisourcegeneregulatorynetworkinference AT zacharymwestrick fusedregressionformultisourcegeneregulatorynetworkinference AT christianlmuller fusedregressionformultisourcegeneregulatorynetworkinference AT lionelchristiaen fusedregressionformultisourcegeneregulatorynetworkinference AT richardbonneau fusedregressionformultisourcegeneregulatorynetworkinference |
_version_ |
1714667107393732608 |