Statistical inference and reverse engineering of gene regulatory networks from observational expression data

Frank Emmert-Streib; Galina V. Glazko; Gökmen Altay; Ricardo de Matos Simoes

doi:10.3389/fgene.2012.00008

Statistical inference and reverse engineering of gene regulatory networks from observational expression data

Frank Emmert-Streib^*
, Galina V. Glazko
, Gökmen Altay
, Ricardo de Matos Simoes

^*Corresponding author for this work

Queen's University Belfast
University of Cambridge
University of Arkansas for Medical Sciences
Bahcesehir University

Research output: Contribution to journal › Review article › peer-review

104 Citations (Scopus)

Abstract

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.

Original language	English
Article number	Article 8
Journal	Frontiers in Genetics
Volume	3
Issue number	FEB
DOIs	https://doi.org/10.3389/fgene.2012.00008
Publication status	Published - 3 Feb 2012
Externally published	Yes

Keywords

Bayesian network
Causal relations
Directed acyclic graphs
Gene regulatory networks
Information-theory methods
Reverse engineering
Statistical inference

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10.3389/fgene.2012.00008

Cite this

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abstract = "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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AU - Emmert-Streib, Frank

AU - Glazko, Galina V.

AU - Altay, Gökmen

AU - Simoes, Ricardo de Matos

PY - 2012/2/3

Y1 - 2012/2/3

N2 - 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.

AB - 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.

KW - Bayesian network

KW - Causal relations

KW - Directed acyclic graphs

KW - Gene regulatory networks

KW - Information-theory methods

KW - Reverse engineering

KW - Statistical inference

UR - https://www.scopus.com/pages/publications/84859112389

U2 - 10.3389/fgene.2012.00008

DO - 10.3389/fgene.2012.00008

M3 - Review article

AN - SCOPUS:84859112389

SN - 1664-8021

VL - 3

JO - Frontiers in Genetics

JF - Frontiers in Genetics

IS - FEB

M1 - Article 8

ER -

Statistical inference and reverse engineering of gene regulatory networks from observational expression data

Abstract

Keywords

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