S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, Basic local alignment search tool, Journal of Molecular Biology, vol.215, issue.3, pp.403-410, 1990.
DOI : 10.1016/S0022-2836(05)80360-2

A. Ben-hur and D. L. Brutlag, Remote homology detection: a motif based approach, ISMB (Supplement of Bioinformatics), pp.26-33, 2003.
DOI : 10.1093/bioinformatics/btg1002

T. G. Dietterich, R. H. Lathrop, and T. Lozano-pérez, Solving the multiple instance problem with axis-parallel rectangles, Artificial Intelligence, vol.89, issue.1-2, pp.31-71, 1997.
DOI : 10.1016/S0004-3702(96)00034-3

G. Fu, X. Nan, H. Liu, R. Y. Patel, P. R. Daga et al., Implementation of multiple-instance learning in drug activity prediction, BMC Bioinformatics, vol.13, issue.Suppl 15, pp.13-15, 2012.
DOI : 10.1016/0005-2795(75)90109-9

N. Gao, B. Ma, Y. Zhang, Q. Song, L. Chen et al., Gene Expression Analysis of Four Radiation-resistant Bacteria, Genomics Insights, vol.2, pp.11-22, 2009.
DOI : 10.4137/GEI.S2380

J. Han, M. Kamber, and J. Pei, Data Mining, 2011.
DOI : 10.1145/233269.233324
URL : https://hal.archives-ouvertes.fr/hal-01534761

K. Liolios, K. Mavromatis, N. Tavernarakis, and N. C. Kyrpides, The Genomes On Line Database (GOLD) in 2007: status of genomic and metagenomic projects and their associated metadata, Nucleic Acids Research, vol.73, issue.10, pp.475-479, 2008.
DOI : 10.1128/AEM.02985-06

O. Maron and T. L. Pérez, A Framework for Multiple-Instance Learning, Advances in Neural Information Processing Systems, pp.570-576, 1998.

H. Nishida and M. Nishiyama, International Journal of Evolutionary Biology, vol.18, issue.5, 2012.
DOI : 10.1016/j.ygeno.2011.08.001

N. Ohtani, M. Tomita, and M. Itaya, An Extreme Thermophile, Thermus thermophilus, Is a Polyploid Bacterium, Journal of Bacteriology, vol.192, issue.20, pp.5499-505, 2010.
DOI : 10.1128/JB.00662-10

M. Omelchenko, Y. Wolf, E. Gaidamakova, V. Matrosova, A. Vasilenko et al., Comparative genomics of thermus thermophilus and deinococcus radiodurans : divergent routes of adaptation to thermophily and radiation resistance, BMC Evolutionary Biology, vol.5, issue.1, pp.1-22, 2005.

M. V. Omelchenko, Y. I. Wolf, E. K. Gaidamakova, V. Y. Matrosova, A. Vasilenko et al., Comparative genomics of Thermus thermophilus and Deinococcus radiodurans: divergent routes of adaptation to thermophily and radiation resistance, BMC Evolutionary Biology, vol.5, issue.1, p.57, 2005.
DOI : 10.1186/1471-2148-5-57

R. Saidi, S. Aridhi, E. M. Nguifo, and M. Maddouri, Feature extraction in protein sequences classification, Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB '12, pp.683-689, 2012.
DOI : 10.1145/2382936.2383060

R. Saidi, M. Maddouri, and E. M. Nguifo, Protein sequences classification by means of feature extraction with substitution matrices, BMC Bioinformatics, vol.11, issue.1, p.175, 2010.
DOI : 10.1186/1471-2105-11-175
URL : https://hal.archives-ouvertes.fr/hal-00459421

H. Sghaier, K. Ghedira, A. Benkahla, and I. Barkallah, Basal DNA repair machinery is subject to positive selection in ionizing-radiation-resistant bacteria, BMC Genomics, vol.9, issue.1, p.297, 2008.
DOI : 10.1186/1471-2164-9-297
URL : https://hal.archives-ouvertes.fr/hal-01358559

J. Wang and J. Zucker, Solving the multiple-instance problem: A lazy learning approach, Proc. 17th International Conf. on Machine Learning, pp.1119-1125, 2000.

H. Yamakawa, K. Maruhashi, and Y. Nakao, Predicting Types of Protein-Protein Interactions Using a Multiple-Instance Learning Model, New Frontiers in Artificial Intelligence, pp.42-53
DOI : 10.1007/978-3-540-69902-6_5