Inference of disease-specific gene interaction network using a Bayesian network learned by genetic algorithm

Daye Jeong, Yunku Yeu, Jaegyoon Ahn, Youngmi Yoon, Sang Hyun Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An important goal of systems biology is to understand and identify mechanisms of the human body system. Genes play functional roles in the context of complex pathways. Analysis of genes as networks is therefore important to understand whole system mechanisms. Biological activities are governed by various signaling networks. The advent of high-throughput technologies has made it possible to obtain biological information on a genome-wide scale. Genetic interactions have been identified from high-throughput data such as microarray data using Bayesian networks. In this paper, we infer the disease-specific gene interaction network using a Bayesian network, which is robust to noise in the data. We apply a genetic algorithm to learn the Bayesian network. We use heterogeneous data, including microarray, protein-protein interaction (PPI), and HumanNET data to learn and score the network. We also exploit single nucleotide polymorphism (SNP) data to infer disease-specific genetic interaction network. We included SNPs as this data may help detect weak signals related to genetic variation In this paper, we reconstruct interactions between pathway genes using our method. We confirm that our method has statistically significant reconstruction power by applying it to Type II diabetes data. Importantly, using Alzheimer disease data, we infer an unreported interaction between a SNP and a disease-related gene. Copyright is held by the owner/author(s).

Original languageEnglish
Title of host publication2015 Symposium on Applied Computing, SAC 2015
EditorsDongwan Shin
PublisherAssociation for Computing Machinery
Pages47-53
Number of pages7
ISBN (Electronic)9781450331968
DOIs
Publication statusPublished - 2015 Apr 13
Event30th Annual ACM Symposium on Applied Computing, SAC 2015 - Salamanca, Spain
Duration: 2015 Apr 132015 Apr 17

Publication series

NameProceedings of the ACM Symposium on Applied Computing
Volume13-17-April-2015

Other

Other30th Annual ACM Symposium on Applied Computing, SAC 2015
CountrySpain
CitySalamanca
Period15/4/1315/4/17

Fingerprint

Bayesian networks
Genes
Genetic algorithms
Microarrays
Nucleotides
Polymorphism
Throughput
Proteins
Medical problems
Bioactivity

All Science Journal Classification (ASJC) codes

  • Software

Cite this

Jeong, D., Yeu, Y., Ahn, J., Yoon, Y., & Park, S. H. (2015). Inference of disease-specific gene interaction network using a Bayesian network learned by genetic algorithm. In D. Shin (Ed.), 2015 Symposium on Applied Computing, SAC 2015 (pp. 47-53). (Proceedings of the ACM Symposium on Applied Computing; Vol. 13-17-April-2015). Association for Computing Machinery. https://doi.org/10.1145/2695664.2695944
Jeong, Daye ; Yeu, Yunku ; Ahn, Jaegyoon ; Yoon, Youngmi ; Park, Sang Hyun. / Inference of disease-specific gene interaction network using a Bayesian network learned by genetic algorithm. 2015 Symposium on Applied Computing, SAC 2015. editor / Dongwan Shin. Association for Computing Machinery, 2015. pp. 47-53 (Proceedings of the ACM Symposium on Applied Computing).
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abstract = "An important goal of systems biology is to understand and identify mechanisms of the human body system. Genes play functional roles in the context of complex pathways. Analysis of genes as networks is therefore important to understand whole system mechanisms. Biological activities are governed by various signaling networks. The advent of high-throughput technologies has made it possible to obtain biological information on a genome-wide scale. Genetic interactions have been identified from high-throughput data such as microarray data using Bayesian networks. In this paper, we infer the disease-specific gene interaction network using a Bayesian network, which is robust to noise in the data. We apply a genetic algorithm to learn the Bayesian network. We use heterogeneous data, including microarray, protein-protein interaction (PPI), and HumanNET data to learn and score the network. We also exploit single nucleotide polymorphism (SNP) data to infer disease-specific genetic interaction network. We included SNPs as this data may help detect weak signals related to genetic variation In this paper, we reconstruct interactions between pathway genes using our method. We confirm that our method has statistically significant reconstruction power by applying it to Type II diabetes data. Importantly, using Alzheimer disease data, we infer an unreported interaction between a SNP and a disease-related gene. Copyright is held by the owner/author(s).",
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Jeong, D, Yeu, Y, Ahn, J, Yoon, Y & Park, SH 2015, Inference of disease-specific gene interaction network using a Bayesian network learned by genetic algorithm. in D Shin (ed.), 2015 Symposium on Applied Computing, SAC 2015. Proceedings of the ACM Symposium on Applied Computing, vol. 13-17-April-2015, Association for Computing Machinery, pp. 47-53, 30th Annual ACM Symposium on Applied Computing, SAC 2015, Salamanca, Spain, 15/4/13. https://doi.org/10.1145/2695664.2695944

Inference of disease-specific gene interaction network using a Bayesian network learned by genetic algorithm. / Jeong, Daye; Yeu, Yunku; Ahn, Jaegyoon; Yoon, Youngmi; Park, Sang Hyun.

2015 Symposium on Applied Computing, SAC 2015. ed. / Dongwan Shin. Association for Computing Machinery, 2015. p. 47-53 (Proceedings of the ACM Symposium on Applied Computing; Vol. 13-17-April-2015).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Jeong D, Yeu Y, Ahn J, Yoon Y, Park SH. Inference of disease-specific gene interaction network using a Bayesian network learned by genetic algorithm. In Shin D, editor, 2015 Symposium on Applied Computing, SAC 2015. Association for Computing Machinery. 2015. p. 47-53. (Proceedings of the ACM Symposium on Applied Computing). https://doi.org/10.1145/2695664.2695944