Cited By
View all- Shang JWang XWu XSun YDing QLiu JZhang H(2019)A Review of Ant Colony Optimization Based Methods for Detecting Epistatic InteractionsIEEE Access10.1109/ACCESS.2019.28946767(13497-13509)Online publication date: 2019
A New Epistasis Detecting Algorithm Based on Ant Colony Optimization
Authors: 
Zhihui Zhou, Guixia Liu, Lingtao Su, Liang Han, Lun YanAuthors Info & Claims
Zhihui Zhou, Guixia Liu, Lingtao Su, Liang Han, Lun YanAuthors Info & ClaimsPages 226 - 231
Abstract
The rapid developments of chip-based technology have greatly improved human genetics and made routine the access of thousands of single nucleotide polymorphisms (SNPs) contributing to an informatics challenge. The characterization and interpretation of genes and gene-gene interactions that affect the susceptibility of common, complex multifactorial diseases is a computational and statistical challenge in genome-wide association studies (GWAS). Various methods have been proposed, but they have difficulty to be directly applied to GWAS caused by excessive search space and intensive computational burden. In this paper, we propose an ant colony optimization (ACO) based algorithm by combining the pheromone updating rule with the heuristic information. We tested power performance of our algorithm by conducting sufficient experiments including a wide range of simulated datasets experiments and a real genome-wide dataset experiment. Experimental results demonstrate that our algorithm is time efficient and gain good performance in the term of the power of prediction accuracy.
References
[1]
http://en.wikipedia.org/wiki/Diabetes_mellitus_type_1.
[2]
D. Botstein, R. L. White, M. Skolnick, and R. W. Davis. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. American journal of human genetics, 32(3):314, 1980.
[3]
M. Dorigo, V. Maniezzo, A. Colorni, and V. Maniezzo. Positive feedback as a search strategy. 1991.
[4]
C. S. Greene, B. C. White, and J. H. Moore. Ant colony optimization for genome-wide genetic analysis. In Ant Colony Optimization and Swarm Intelligence, pages 37--47. Springer, 2008.
[5]
K. Kira and L. A. Rendell. A practical approach to feature selection. In Proceedings of the ninth international workshop on Machine learning, pages 249--256. Morgan Kaufmann Publishers Inc., 1992.
[6]
I. Kononenko. Estimating attributes: analysis and extensions of relief. In Machine Learning: ECML-94, pages 171--182. Springer, 1994.
[7]
J. Marchini, P. Donnelly, and L. R. Cardon. Genome-wide strategies for detecting multiple loci that influence complex diseases. Nature genetics, 37(4):413--417, 2005.
[8]
J. Millstein, D. V. Conti, F. D. Gilliland, and W. J. Gauderman. A testing framework for identifying susceptibility genes in the presence of epistasis. The American Journal of Human Genetics, 78(1):15--27, 2006.
[9]
V. Ü. Onay, L. Briollais, J. A. Knight, E. Shi, Y. Wang, S. Wells, H. Li, I. Rajendram, I. L. Andrulis, and H. Ozcelik. Snp-snp interactions in breast cancer susceptibility. BMC cancer, 6(1):114, 2006.
[10]
R. S. Parpinelli, H. S. Lopes, and A. A. Freitas. An ant colony based system for data mining: applications to medical data. In Proceedings of the genetic and evolutionary computation conference (GECCO-2001), pages 791--797. Citeseer, 2001.
[11]
M. D. Ritchie, L. W. Hahn, N. Roodi, L. R. Bailey, W. D. Dupont, F. F. Parl, and J. H. Moore. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. The American Journal of Human Genetics, 69(1):138--147, 2001.
[12]
M. Robnik-Šikonja and I. Kononenko. Theoretical and empirical analysis of relieff and rrelieff. Machine learning, 53(1-2):23--69, 2003.
[13]
W. Tang, X. Wu, R. Jiang, and Y. Li. Epistatic module detection for case-control studies: a bayesian model with a gibbs sampling strategy. PLoS genetics, 5(5):e1000464, 2009.
[14]
X. Wan, C. Yang, Q. Yang, H. Xue, N. L. Tang, and W. Yu. Megasnphunter: a learning approach to detect disease predisposition snps and high level interactions in genome wide association study. BMC bioinformatics, 10(1):13, 2009.
[15]
X. Wan, C. Yang, Q. Yang, H. Xue, N. L. Tang, and W. Yu. Predictive rule inference for epistatic interaction detection in genome-wide association studies. Bioinformatics, 26(1):30--37, 2010.
[16]
S. M. Williams, M. Ritchie, J. Phillips Iii, E. Dawson, M. Prince, E. Dzhura, A. Willis, A. Semenya, M. Summar, B. White, et al. Multilocus analysis of hypertension: a hierarchical approach. Human heredity, 57(1):28--38, 2004.
[17]
Y. Zhang and J. S. Liu. Bayesian inference of epistatic interactions in case-control studies. Nature genetics, 39(9):1167--1173, 2007.
[18]
Y. Zhang, J. Zhang, and J. S. Liu. Block-based bayesian epistasis association mapping with application to wtccc type 1 diabetes data. The annals of applied statistics, 5(3):2052, 2011.
[19]
Z. Zhou, G. Liu, L. Su, L. Yan, and L. Han. Cchi: An efficient cloud epistasis test model in human genome wide association studies. In Biomedical Engineering and Informatics (BMEI), 2013 6th International Conference on, pages 787--791. IEEE, 2013.
Index Terms
- A New Epistasis Detecting Algorithm Based on Ant Colony Optimization
Recommendations
Ant colony optimisation to identify genetic variant association with type 2 diabetes
Around 1.8 million people in the UK have type 2 diabetes, representing about 90% of all diabetes cases in the UK. Genome wide association studies have recently implicated several new genes that are likely to be associated with this disease. However, ...
Ant colony optimization with an automatic adjustment mechanism for detecting epistatic interactions
Graphical abstractDisplay Omitted
Highlights- A novel ant colony optimization with an automatic adjustment mechanism (AA-ACO) is proposed.
- Automatic adjustment mechanism automatically can adjust the behaviour of artificial ants according to the real-time feedback information.
- ...
AbstractSingle Nucleotide polymorphisms (SNPs) are usually used as biomarkers for research and analysis of genome-wide association study (GWAS). Moreover, the epistatic interaction of SNPs is an important factor in determining the susceptibility of ...
A new approach to detect epistasis utilizing parallel implementation of ant colony optimization by MapReduce framework
Genome-wide association studies GWAS involve the detection and interpretation of epistasis, which is responsible for the ‘missing heritability’ and influences common complex disease susceptibility. Many epistasis detection algorithms cannot be directly ...
Comments
Information & Contributors
Information
Published In
July 2014
430 pages
ISBN:9781450328104
DOI:10.1145/2632856
- General Chairs:
- Hanzi Wang,
- Larry Davis,
- Program Chairs:
- Wenwu Zhu,
- Stephan Kopf,
- Yanyun Qu,
- Publications Chairs:
- Jun Yu,
- Jitao Sang,
- Tao Mei
Copyright © 2014 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- NSF of China: National Natural Science Foundation of China
- Beijing ACM SIGMM Chapter
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 July 2014
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICIMCS '14
ICIMCS '14: International Conference on Internet Multimedia Computing and Service
July 10 - 12, 2014
Xiamen, China
Acceptance Rates
Overall Acceptance Rate 163 of 456 submissions, 36%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 84Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 21 Dec 2024
Other Metrics
Citations
Cited By
View all- Shang JWang XWu XSun YDing QLiu JZhang H(2019)A Review of Ant Colony Optimization Based Methods for Detecting Epistatic InteractionsIEEE Access10.1109/ACCESS.2019.28946767(13497-13509)Online publication date: 2019
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in