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 language | English |
|---|---|
| Title of host publication | 2015 Symposium on Applied Computing, SAC 2015 |
| Editors | Dongwan Shin |
| Publisher | Association for Computing Machinery |
| Pages | 47-53 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781450331968 |
| DOIs | |
| Publication status | Published - 2015 Apr 13 |
| Event | 30th Annual ACM Symposium on Applied Computing, SAC 2015 - Salamanca, Spain Duration: 2015 Apr 13 → 2015 Apr 17 |
Publication series
| Name | Proceedings of the ACM Symposium on Applied Computing |
|---|---|
| Volume | 13-17-April-2015 |
Other
| Other | 30th Annual ACM Symposium on Applied Computing, SAC 2015 |
|---|---|
| Country | Spain |
| City | Salamanca |
| Period | 15/4/13 → 15/4/17 |
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All Science Journal Classification (ASJC) codes
- Software
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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 proceeding › Conference contribution
TY - GEN
T1 - Inference of disease-specific gene interaction network using a Bayesian network learned by genetic algorithm
AU - Jeong, Daye
AU - Yeu, Yunku
AU - Ahn, Jaegyoon
AU - Yoon, Youngmi
AU - Park, Sang Hyun
PY - 2015/4/13
Y1 - 2015/4/13
N2 - 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).
AB - 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).
UR - http://www.scopus.com/inward/record.url?scp=84955467695&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84955467695&partnerID=8YFLogxK
U2 - 10.1145/2695664.2695944
DO - 10.1145/2695664.2695944
M3 - Conference contribution
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 47
EP - 53
BT - 2015 Symposium on Applied Computing, SAC 2015
A2 - Shin, Dongwan
PB - Association for Computing Machinery
ER -