research-article

A systems biology approach for the identification of significantly perturbed genes

Authors:

Michele Donato,

Sorin DraghiciAuthors Info & Claims

BCB '15: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

Pages 423 - 432

https://doi.org/10.1145/2808719.2808763

Published: 09 September 2015 Publication History

Abstract

Identifying the genes involved in the mechanisms differentiating two phenotypes is a crucial step in the analysis of high-throughput gene expression experiments. Although in the last decade several approaches have been developed in order to address this challenge, a number of important issues remain open. Even the most widely used approaches fail to incorporate information about known interactions among genes, and they often fail to yield reproducible results across similar experiments, both in terms of the set of genes and in terms of the mechanisms related to those genes. Here we propose a novel systems biology approach able to i) identify the genes that are involved in a biological mechanism relevant to the condition in analysis and ii) yield reproducible results across multiple data sets related to the same condition. This is achieved by using gene expression levels and existing knowledge about the interactions among genes. We apply our method on four data sets describing two conditions, and we compare our results with the results of the classical approach of identifying genes based on their differential expression and p-values. The results show that our approach is better at identifying genes that are involved in the mechanisms relevant to the phenotype in analysis, as well as producing more consistent results across data sets describing the same biological condition.

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Cited By

Hanna EEl Hasbani GAzar D(2024)Ant colony optimization for the identification of dysregulated gene subnetworks from expression dataBMC Bioinformatics10.1186/s12859-024-05871-x25:1Online publication date: 1-Aug-2024
https://doi.org/10.1186/s12859-024-05871-x
Shafi ANguyen TPeyvandipour ANguyen HDraghici S(2019)A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene SignaturesFrontiers in Genetics10.3389/fgene.2019.0015910Online publication date: 19-Mar-2019
https://doi.org/10.3389/fgene.2019.00159
Shah SBraun R(2019)GeneSurrounder: network-based identification of disease genes in expression dataBMC Bioinformatics10.1186/s12859-019-2829-y20:1Online publication date: 6-May-2019
https://doi.org/10.1186/s12859-019-2829-y
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Index Terms

A systems biology approach for the identification of significantly perturbed genes
1. Applied computing
  1. Life and medical sciences

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cover image ACM Conferences

BCB '15: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

September 2015

683 pages

ISBN:9781450338530

DOI:10.1145/2808719

Copyright © 2015 ACM.

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Published: 09 September 2015

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BCB '15: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine

September 9 - 12, 2015

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BCB '15 Paper Acceptance Rate 48 of 141 submissions, 34%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Hanna EEl Hasbani GAzar D(2024)Ant colony optimization for the identification of dysregulated gene subnetworks from expression dataBMC Bioinformatics10.1186/s12859-024-05871-x25:1Online publication date: 1-Aug-2024
https://doi.org/10.1186/s12859-024-05871-x
Shafi ANguyen TPeyvandipour ANguyen HDraghici S(2019)A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene SignaturesFrontiers in Genetics10.3389/fgene.2019.0015910Online publication date: 19-Mar-2019
https://doi.org/10.3389/fgene.2019.00159
Shah SBraun R(2019)GeneSurrounder: network-based identification of disease genes in expression dataBMC Bioinformatics10.1186/s12859-019-2829-y20:1Online publication date: 6-May-2019
https://doi.org/10.1186/s12859-019-2829-y
Shafi ANguyen TPeyvandipour ADraghici S(2019)GSMA: an approach to identify robust global and test Gene Signatures using Meta-AnalysisBioinformatics10.1093/bioinformatics/btz56136:2(487-495)Online publication date: 22-Jul-2019
https://doi.org/10.1093/bioinformatics/btz561
Fang HLi XZan XShen LMa RLiu W(2017)Signaling pathway impact analysis by incorporating the importance and specificity of genes (SPIA-IS)Computational Biology and Chemistry10.1016/j.compbiolchem.2017.09.00971:C(236-244)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1016/j.compbiolchem.2017.09.009

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