Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks

Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks

Motivation: Inferring global regulatory networks (GRNs) from genome-wide data is a computational challenge central to the field of systems biology. Although the primary data currently used to infer GRNs consist of gene expression and proteomics measurements, there is a growing abundance of alternate...

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Main Authors: Greenfield, Alex, Hafemeister, Christoph, Bonneau, Richard
Format: Online
Language:English
Published: Oxford University Press 2013
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3624811/
id pubmed-3624811
recordtype oai_dc
spelling pubmed-36248112013-04-12 Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks Greenfield, Alex Hafemeister, Christoph Bonneau, Richard Original Papers Motivation: Inferring global regulatory networks (GRNs) from genome-wide data is a computational challenge central to the field of systems biology. Although the primary data currently used to infer GRNs consist of gene expression and proteomics measurements, there is a growing abundance of alternate data types that can reveal regulatory interactions, e.g. ChIP-Chip, literature-derived interactions, protein–protein interactions. GRN inference requires the development of integrative methods capable of using these alternate data as priors on the GRN structure. Each source of structure priors has its unique biases and inherent potential errors; thus, GRN methods using these data must be robust to noisy inputs. Oxford University Press 2013-04-15 2013-03-21 /pmc/articles/PMC3624811/ /pubmed/23525069 http://dx.doi.org/10.1093/bioinformatics/btt099 Text en © The Author 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Greenfield, Alex
Hafemeister, Christoph
Bonneau, Richard
spellingShingle Greenfield, Alex
Hafemeister, Christoph
Bonneau, Richard
Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks
author_facet Greenfield, Alex
Hafemeister, Christoph
Bonneau, Richard
author_sort Greenfield, Alex
title Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks
title_short Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks
title_full Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks
title_fullStr Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks
title_full_unstemmed Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks
title_sort robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks
description Motivation: Inferring global regulatory networks (GRNs) from genome-wide data is a computational challenge central to the field of systems biology. Although the primary data currently used to infer GRNs consist of gene expression and proteomics measurements, there is a growing abundance of alternate data types that can reveal regulatory interactions, e.g. ChIP-Chip, literature-derived interactions, protein–protein interactions. GRN inference requires the development of integrative methods capable of using these alternate data as priors on the GRN structure. Each source of structure priors has its unique biases and inherent potential errors; thus, GRN methods using these data must be robust to noisy inputs.
publisher Oxford University Press
publishDate 2013
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3624811/
_version_ 1611969773101383680

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