- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
BioMed Research International
Volume 2013 (2013), Article ID 432375, 13 pages
A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology
Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
Received 17 July 2013; Revised 26 August 2013; Accepted 27 August 2013
Academic Editor: Jielin Sun
Copyright © 2013 Ching Lee Koo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- R. Sharan, “Analysis of Biological Networks: Genetic Interaction Networks, 1-38 [PDF file],” 2007http://www.cs.tau.ac.il/~roded/courses/bnet-a06/lec12.pdf.
- N. J. Nilsson, Artificial Intelligence: A New Synthesis, 1998.
- B. A. McKinney, D. M. Reif, M. D. Ritchie, and J. H. Moore, “Machine learning for detecting gene-gene interactions: a review,” Applied Bioinformatics, vol. 5, no. 2, pp. 77–88, 2006.
- N. E. Hardison and A. A. Motsinger-Reif, “The power of quantitative grammatical evolution neural networks to detect gene-gene interactions,” in Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO '11), pp. 299–306, July 2011.
- I. A. Basheer and M. Hajmeer, “Artificial neural networks: fundamentals, computing, design, and application,” Journal of Microbiological Methods, vol. 43, no. 1, pp. 3–31, 2000.
- L. C. Tsilo, Protein secondary structure prediction using neural networks and support vector machines [M.S. thesis], Rhodes University, Grahamstown, South Africa, 2008.
- A. A. Motsinger-Reif and M. D. Ritchie, “Neural networks for genetic epidemiology: past, present, and future,” BioData Mining, vol. 1, pp. 1–15, 2008.
- D. Curtis, “Comparison of artificial neural network analysis with other multimarker methods for detecting genetic association,” BMC Genetics, vol. 8, p. 49, 2007.
- D. Curtis, B. V. North, and P. C. Sham, “Use of an artificial neural network to detect association between a disease and multiple marker genotypes,” Annals of Human Genetics, no. 1, pp. 95–107, 2001.
- M. D. Ritchie, C. S. Coffey, and J. H. Moore, “Genetic programming neural networks as a bioinformatics tool for human genetics,” in Genetic and Evolutionary Computation, G. Raidl, R. Poli, W. Banzhaf, et al., Eds., vol. 3102 of Lecture Notes in Computer Science, pp. 438–448, Springer, Berlin, Germany, 2004.
- M. D. Ritchie, B. C. White, J. S. Parker, L. W. Hahn, and J. H. Moore, “Optimization of neural network architecture using genetic programming improves detection and modelling of gene-gene interactions in studies of human diseases,” BMC Bioinformatics, vol. 4, p. 28, 2003.
- Y. Tomita, S. Tomida, Y. Hasegawa et al., “Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma,” BMC Bioinformatics, vol. 5, p. 120, 2004.
- E. Keedwell and A. Narayanan, “Discovering gene networks with a neural-genetic hybrid,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 2, no. 3, pp. 231–242, 2005.
- A. A. Motsinger, S. L. Lee, G. Mellick, and M. D. Ritchie, “GPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease,” BMC Bioinformatics, vol. 7, p. 39, 2006.
- M. D. Ritchie, A. A. Motsinger, W. S. Bush, C. S. Coffey, and J. H. Moore, “Genetic programming neural networks: a powerful bioinformatics tool for human genetics,” Applied Soft Computing Journal, vol. 7, no. 1, pp. 471–479, 2007.
- A. A. Motsinger-Reif, T. J. Fanelli, A. C. Davis, and M. D. Ritchie, “Power of grammatical evolution neural networks to detect gene-gene interactions in the presence of error,” BMC Research Notes, vol. 1, p. 65, 2008.
- F. Günther, N. Wawro, and K. Bammann, “Neural networks for modeling gene-gene interactions in association studies,” BMC Genetics, vol. 10, p. 87, 2009.
- S. D. Turner, S. M. Dudek, and M. D. Ritchie, “ATHENA: a knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci,” BioData Mining, vol. 3, no. 1, p. 5, 2010.
- S. Chen, J. Sun, L. Dimitrov et al., “A support vector machine approach for detecting gene-gene interaction,” Genetic Epidemiology, vol. 32, no. 2, pp. 152–167, 2008.
- N. Barakat and A. P. Bradley, “Rule extraction from support vector machines: a review,” Neurocomputing, vol. 74, no. 1–3, pp. 178–190, 2010.
- P. V. Missiuro, Predicting genetic interactions in Caenorhabditiselegans using machine learning [Ph.D. thesis], Massachusetts Institute of Technology, Cambridge, Mass, USA, 2010.
- C. Hsu, C. Chang, and C. Lin, “A Practical Guide to Support Vector Classification, 1-16, Retrieved from,” 2010, http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.
- N. Matchenko-Shimko and M. P. Dubé, “Gene-gene interaction tests using SVM and neural network modeling,” in Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 90–97, September 2006.
- A. Özgür, T. Vu, G. Erkan, and D. R. Radev, “Identifying gene-disease associations using centrality on a literature mined gene-interaction network,” Bioinformatics, vol. 24, no. 13, pp. i277–i285, 2008.
- Y. Shen, Z. Liu, and J. Ott, “Detecting gene-gene interactions using support vector machines with L 1 penalty,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW '10), pp. 309–311, December 2010.
- H. Ban, J. Y. Heo, K. Oh, and K. Park, “Identification of Type 2 Diabetes-associated combination of SNPs using Support Vector Machine,” BMC Genetics, vol. 11, p. 26, 2010.
- Y. Fang and Y. Chiu, “SVM-based generalized multifactor dimensionality reduction approaches for detecting gene-gene interactions in family studies,” Genetic Epidemiology, vol. 36, no. 2, pp. 88–98, 2012.
- H. Zhang, H. Wang, Z. Dai, M. Chen, and Z. Yuan, “Improving accuracy for cancer classification with a new algorithm for genes selection,” BMC Bioinformatics, vol. 13, pp. 1–20, 2012.
- S. Marvel and A. Motsinger-Reif, “Grammatical evolution support vector machines for predicting human genetic disease association,” in Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Conference (GECCO '12), pp. 595–598, 2012.
- J. H. Moore, F. W. Asselbergs, and S. M. Williams, “Bioinformatics challenges for genome-wide association studies,” Bioinformatics, vol. 26, no. 4, Article ID btp713, pp. 445–455, 2010.
- R. Upstill-Goddard, D. Eccles, J. Reige, and A. Collins, “Machine learning approaches for the discovery of gene-gene interactions in disease data,” Briefing in Bioinformatics, vol. 14, no. 2, p. 251, 2013.
- S. J. Winham, C. L. Colby, R. R. Freimuth et al., “SNP interaction detection with random forests in high-dimensional genetic data,” BMC Bioinformatics, vol. 13, p. 164, 2012.
- K. L. Lunetta, L. B. Hayward, J. Segal, and P. van Eerdewegh, “Screening large-scale association study data: exploiting interactions using random forests,” BMC Genetics, vol. 5, p. 32, 2004.
- R. Jiang, W. Tang, X. Wu, and W. Fu, “A random forest approach to the detection of epistatic interactions in case-control studies,” BMC Bioinformatics, vol. 10, supplement 1, p. S65, 2009.
- D. F. Schwarz, I. R. König, and A. Ziegler, “On safari to random Jungle: a fast implementation of random forests for high-dimensional data,” Bioinformatics, vol. 26, no. 14, Article ID btq257, pp. 1752–1758, 2010.
- C. Y. Liu, H. H. Ackerman, and J. P. Carulli, “A genome-wide screen of gene-gene interactions for rheumatoid arthritis susceptibility,” Human Genetics, vol. 129, no. 5, pp. 473–485, 2011.
- Q. X. Pan, T. Hu, J. D. Malley, A. S. Andrew, M. R. Karagas, and J. H. Moore, “Supervising random forest using attribute interaction networks,” in Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, L. Vanneschi, W. S. Bush, and M. Giacobini, Eds., vol. 7833 of Lecture Notes in Computer Science, pp. 104–116, 2013.
- A. Staiano, M. D. Di Taranto, E. Bloise et al., “Investigation of single nucleotide polymorphisms associated to familial combined Hyperlipidemia with random forests,” in Neural Nets and Surroundingset, B. Apolloni, S. Bassis, A. Esposito, and F. C. Morabito, Eds., vol. 19, pp. 169–178, 2013.
- X. Chen and H. Ishwaran, “Pathway hunting by random survival forests,” Bioinformatics, vol. 29, no. 1, pp. 99–105, 2013.
- S. K. Musani, D. Shriner, N. Liu et al., “Detection of gene x gene interactions in genome-wide association studies of human population data,” Human Heredity, vol. 63, no. 2, pp. 67–84, 2007.
- P. S. Wasan, M. Uttamchandani, S. Mochhala, V. B. Yap, and P. H. Yap, “Application of statistics and machine learning for risk stratification of heritable cardiac arrhythmias,” Expert Systems with Applications, vol. 40, no. 7, pp. 2476–2486, 2013.