Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data

In this paper, we present a systematic and conceptual overview of methods for inferring gene regulatory networks from observational gene expression data. Further, we discuss two classic approaches to infer causal structures and compare them with contemporary methods by providing a conceptual categor...

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Detalles Bibliográficos
Autores principales: Emmert-Streib, Frank, Glazko, Galina V., Altay, Gökmen, de Matos Simoes, Ricardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Research Foundation 2012
Materias:
Genetics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3271232/
https://www.ncbi.nlm.nih.gov/pubmed/22408642
http://dx.doi.org/10.3389/fgene.2012.00008
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author Emmert-Streib, Frank
Glazko, Galina V.
Altay, Gökmen
de Matos Simoes, Ricardo
author_facet Emmert-Streib, Frank
Glazko, Galina V.
Altay, Gökmen
de Matos Simoes, Ricardo
author_sort Emmert-Streib, Frank
collection PubMed
description In this paper, we present a systematic and conceptual overview of methods for inferring gene regulatory networks from observational gene expression data. Further, we discuss two classic approaches to infer causal structures and compare them with contemporary methods by providing a conceptual categorization thereof. We complement the above by surveying global and local evaluation measures for assessing the performance of inference algorithms.
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spelling pubmed-32712322012-03-09 Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data Emmert-Streib, Frank Glazko, Galina V. Altay, Gökmen de Matos Simoes, Ricardo Front Genet Genetics In this paper, we present a systematic and conceptual overview of methods for inferring gene regulatory networks from observational gene expression data. Further, we discuss two classic approaches to infer causal structures and compare them with contemporary methods by providing a conceptual categorization thereof. We complement the above by surveying global and local evaluation measures for assessing the performance of inference algorithms. Frontiers Research Foundation 2012-02-03 /pmc/articles/PMC3271232/ /pubmed/22408642 http://dx.doi.org/10.3389/fgene.2012.00008 Text en Copyright © 2012 Emmert-Streib, Glazko, Altay and de Matos Simoes. http://www.frontiersin.org/licenseagreement This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.
spellingShingle Genetics
Emmert-Streib, Frank
Glazko, Galina V.
Altay, Gökmen
de Matos Simoes, Ricardo
Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data
title Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data
title_full Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data
title_fullStr Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data
title_full_unstemmed Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data
title_short Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data
title_sort statistical inference and reverse engineering of gene regulatory networks from observational expression data
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3271232/
https://www.ncbi.nlm.nih.gov/pubmed/22408642
http://dx.doi.org/10.3389/fgene.2012.00008
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