Literature DB >> 22408642

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

Frank Emmert-Streib1, Galina V Glazko, Gökmen Altay, Ricardo de Matos Simoes.   

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.

Entities:  

Keywords:  Bayesian network; causal relations; directed acyclic graphs; gene regulatory networks; information-theory methods; reverse engineering; statistical inference

Year:  2012        PMID： 22408642      PMCID： PMC3271232          DOI： 10.3389/fgene.2012.00008

Source DB:  PubMed          Journal:  Front Genet        ISSN： 1664-8021            Impact factor:   4.599


  78 in total

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  47 in total

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3.  High-Dimensional Bayesian Network Inference From Systems Genetics Data Using Genetic Node Ordering.

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Review 5.  On protocols and measures for the validation of supervised methods for the inference of biological networks.

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6.  Changes in Gene Expression in Space and Time Orchestrate Environmentally Mediated Shaping of Root Architecture.

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7.  Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets.

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9.  Interfacing cellular networks of S. cerevisiae and E. coli: connecting dynamic and genetic information.

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10.  Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: environmental factors.

Authors:  Frank Emmert-Streib
Journal:  PeerJ       Date:  2013-02-12       Impact factor: 2.984
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