T. Hastie, R. Tibshirani, and M. Wainwright, Statistical Learning with Sparsity - The Lasso and Generalizations, Boca Rato, vol.143, 2015.

Y. Ming and L. Yi, Model selection and estimation in regression with grouped variables, J. R. Statist. Soc. B, part, vol.1, pp.68-117, 2006.

L. Meier, S. Van-de-geer, and P. Bhlmann, The group lasso for logistic regression, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.68, issue.1, pp.53-71, 2008.
DOI : 10.1093/oxfordjournals.pan.a004868

URL : http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2007.00627.x/pdf

L. Jacob, G. Obozinski, and -. J. Vert, Group lasso with overlap and graph lasso, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, 2009.
DOI : 10.1145/1553374.1553431

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.7108

A. Beck and M. Teboulle, Gradient-based algorithms with applications to signal-recovery problems, Convex Optimization in Signal Processing and Communications, pp.42-88, 2010.
DOI : 10.1017/CBO9780511804458.003

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.231.2999

M. Gnen and E. Alpaydin, Multiple kernel learning algorithms, J. Mach. Learn. Res, vol.12, pp.2211-2268, 2011.

M. Kloft, U. Brefeld, S. Sonnenburg, and A. Zien, p-norm multiple kernel learning, J. Mach. Learn. Res, vol.12, pp.953-997, 2011.

J. Liu and V. D. Calhoun, A review of multivariate analyses in imaging genetics, Frontiers in Neuroinformatics, vol.70, issue.192, pp.1-11, 2014.
DOI : 10.1016/j.biopsych.2011.04.019

N. K. Batmanghelich, A. Dalca, G. Quon, M. Sabuncu, and P. Golland, Probabilistic Modeling of Imaging, Genetics and Diagnosis, IEEE Transactions on Medical Imaging, vol.35, issue.7, pp.1765-1779, 2016.
DOI : 10.1109/TMI.2016.2527784

O. Kohannim, Discovery and replication of gene influences on brain structure using LASSO regression, Frontiers in Neuroscience, vol.6, issue.115, 2012.
DOI : 10.3389/fnins.2012.00115

M. Silver, Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps, Statistical Applications in Genetics and Molecular Biology, vol.11, issue.1, pp.1-40, 2012.
DOI : 10.2202/1544-6115.1755

M. Silver, E. Janousova, X. Hua, P. M. Thompson, G. Montana et al., Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression, NeuroImage, vol.63, issue.3, pp.1681-1694, 2012.
DOI : 10.1016/j.neuroimage.2012.08.002

H. Wang, F. Nie, H. Huang, S. L. Risacher, A. J. Saykin et al., Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning, Bioinformatics, vol.28, issue.12, pp.127-136, 2012.
DOI : 10.1093/bioinformatics/bts228

URL : https://academic.oup.com/bioinformatics/article-pdf/28/12/i127/751379/bts228.pdf

J. Peng, L. An, X. Zhu, Y. Jin, D. Shen et al., Structured sparse kernel learning for imaging genetics based Alzheimers Disease Diagnosis, MICCAI 2016, pp.70-78, 2016.
DOI : 10.1007/978-3-319-46723-8_9

F. Aiolli and M. Donini, EasyMKL: a scalable multiple kernel learning algorithm, Neurocomputing, vol.169, pp.215-224, 2015.
DOI : 10.1016/j.neucom.2014.11.078

M. Lorenzi, B. Gutman, D. Hibar, A. Altmann, N. Jahanshad et al., Partial least squares modelling for imaging-genetics in Alzheimers Disease: plausibility and generalization In, IEEE ISBI, 2016.

L. Du, A Novel Structure-Aware Sparse Learning Algorithm for Brain Imaging Genetics, MIC- CAI 2014, pp.329-336, 2014.
DOI : 10.1007/978-3-319-10443-0_42