L. Chuang, H. Chang, M. Lin, and C. Yang, Improved branch and bound algorithm for detecting SNP-SNP interactions in breast cancer, Journal of Clinical Bioinformatics, vol.3, issue.1, pp.1-10, 2013.
DOI : 10.1002/cncr.24085

H. Cordell, Detecting gene???gene interactions that underlie human diseases, Nature Reviews Genetics, vol.8, issue.6, pp.392-404, 2009.
DOI : 10.1038/nrg2579

G. Fang, M. Haznadar, W. Wang, H. Yu, M. Steinbach et al., High-Order SNP Combinations Associated with Complex Diseases: Efficient Discovery, Statistical Power and Functional Interactions, PLoS ONE, vol.15, issue.3, 2012.
DOI : 10.1371/journal.pone.0033531.s005

J. Li and Q. Yang, Strong Compound-Risk Factors: Efficient Discovery Through Emerging Patterns and Contrast Sets, IEEE Transactions on Information Technology in Biomedicine, vol.11, issue.5, pp.544-552, 2007.
DOI : 10.1109/TITB.2007.891163

X. Liu, J. Wu, and F. Gu, Jie Wang, and Zengyou He. Discriminative pattern mining and its applications in bioinformatics, Briefings in Bioinformatics, 2014.

Q. Zhang, Q. Long, and J. Ott, AprioriGWAS, a New Pattern Mining Strategy for Detecting Genetic Variants Associated with Disease through Interaction Effects, PLoS Computational Biology, vol.10, issue.6, p.2014
DOI : 10.1371/journal.pcbi.1003627.s003

A. Soulet, C. Raissi, M. Plantevit, and B. Cremilleux, Mining Dominant Patterns in the Sky, 2011 IEEE 11th International Conference on Data Mining, pp.655-664, 2011.
DOI : 10.1109/ICDM.2011.100
URL : https://hal.archives-ouvertes.fr/inria-00623566

W. Ugarte, P. Boizumault, S. Loudni, and B. Cremilleux, Computing Skypattern Cubes Using Relaxation, 2014 IEEE 26th International Conference on Tools with Artificial Intelligence, pp.859-866, 2014.
DOI : 10.1109/ICTAI.2014.132
URL : https://hal.archives-ouvertes.fr/hal-01145902

W. Bush and J. Moore, Genome-wide association studies, Chapter, vol.11, 2012.