A. Abraham, E. Dohmatob, B. Thirion, D. Samaras, and G. Varoquaux, Extracting brain regions from rest fMRI with total-variation constrained dictionary learning, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.607-615, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00853242

A. Ivan and . Adzhubei, A method and server for predicting damaging missense mutations, Nature methods, vol.7, p.248, 2010.

S. Anders and W. Huber, Differential expression analysis for sequence count data, Genome Biol, vol.11, p.106, 2010.

J. Samuel, H. L. Aronson, and . Rehm, Building the foundation for genomics in precision medicine, Nature, vol.526, pp.336-342, 2015.

N. Aronszajn, Theory of reproducing kernels, Transactions of the American mathematical society, vol.68, pp.337-404, 1950.

A. Ashworth, C. J. Lord, and J. S. Reis-filho, Genetic Interactions in Cancer Progression and Treatment, In: Cell, vol.145, pp.30-38, 2011.

L. Sezin-kircali-ata, Y. Ou-yang, . Fang, . Chee-keong, M. Kwoh et al., Integrating node embeddings and biological annotations for genes to predict disease-gene associations, BMC Systems Biology, vol.12, 2018.

S. Athey, G. W. Imbens, and S. Wager, Approximate residual balancing: debiased inference of average treatment effects in high dimensions, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2018.

S. Atwell, Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines, Nature, vol.465, pp.627-631, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00468440

C. Azencott, Machine learning and genomics: precision medicine vs. patient privacy, Philosophical Transactions of the Royal Society A, vol.376, p.2128, 2018.

C. Azencott, D. Grimm, M. Sugiyama, Y. Kawahara, and K. M. Borgwardt, Efficient network-guided multi-locus association mapping with graph cuts, Proceedings of the 21st Annual International Conference on Intelligent Systems for Molecular Biology, vol.29, pp.171-179, 2013.

C. Azencott, A. Ksikes, S. J. Swamidass, J. H. Chen, L. Ralaivola et al., One-to four-dimensional kernels for virtual screening and the prediction of physical, chemical and biological properties, Journal of Chemical Information and Modeling, vol.47, pp.965-974, 2007.

C. Azencott, The inconvenience of data of convenience: computational research beyond post-mortem analyses, Nature methods, vol.14, p.937, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01679028

F. Bach, Structured sparsity-inducing norms through submodular functions, NIPS, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00511310

A. Bagavathi and S. Krishnan, Multi-Net: A Scalable Multiplex Network Embedding Framework, International Conference on Complex Networks and their Applications, pp.119-131, 2018.

A. Barabási, N. Gulbahce, and J. Loscalzo, Network medicine: a network-based approach to human disease, Nature Reviews Genetics, vol.12, pp.56-68, 2011.

A. Barabási and Z. N. Oltvai, Network biology: understanding the cell's functional organization, Nature Reviews Genetics, vol.5, pp.101-113, 2004.

S. E. Baranzini, N. W. Galwey, J. Wang, and P. Khankhanian, Pathway and network-based analysis of genome-wide association studies in multiple sclerosis, Hum Mol Genet, vol.18, pp.2078-2090, 2009.

R. F. Barber and A. Ramdas, The p-filter: multilayer false discovery rate control for grouped hypotheses, J. R. Stat. Soc. B, vol.79, pp.1247-1268, 2017.

K. Brett, P. Beaulieu-jones, J. H. Orzechowski, and . Moore, Mapping Patient Trajectories using Longitudinal Extraction and Deep Learning in the MIMIC-III Critical Care Database, pp.123-132, 2017.

J. P. Claude, H. E. Bélisle, R. L. Romeijn, and . Smith, Hit-and-Run Algorithms for Generating Multivariate Distributions, Mathematics of Operations Research, vol.18, pp.255-266, 1993.

V. Bellon, V. Stoven, and C. Azencott, Multitask feature selection with task descriptors, Pacific Symposium on Biocomputing, vol.21, pp.261-272, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01246697

H. C. Berbee, C. G. Boender, A. H. Rinnooy-ran, C. L. Scheffer, R. L. Smith et al., Hit-and-run algorithms for the identification of nonredundant linear inequalities, Mathematical Programming, vol.37, pp.184-207, 1987.

K. Bleakley and Y. Yamanishi, Supervised prediction of drug-target interactions using bipartite local models, Bioinformatics, vol.25, pp.2397-2403, 2009.

E. V. Bonilla, K. M. Chai, and C. Williams, Multi-task Gaussian process prediction, NIPS 20, pp.153-160, 2007.

Y. Boykov and V. Kolmogorov, An experimental comparison of mincut/max-flow algorithms for energy minimization in vision, IEEE Trans. Pattern Anal. Mach. Intell, vol.26, pp.1124-1137, 2004.

E. A. Boyle, Y. I. Li, and J. K. Pritchard, An Expanded View of Complex Traits: From Polygenic to Omnigenic, Cell 169, vol.7, pp.1177-1186, 2017.

B. Brachi, Linkage and association mapping of Arabidopsis thaliana flowering time in nature, PLoS genetics, vol.6, p.1000940, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00468455

T. S. Brisimi, R. Chen, T. Mela, A. Olshevsky, I. C. Paschalidis et al., Federated learning of predictive models from federated Electronic Health Records, In: International journal of medical informatics, vol.112, pp.59-67, 2018.

Y. Bromberg, Disease gene prioritization, PLoS computational biology, vol.9, p.1002902, 2013.

J. Bruna, W. Zaremba, A. Szlam, and Y. Lecun, Spectral networks and locally connected networks on graphs, 2013.

D. Brzyski, C. B. Peterson, P. Sobczyk, E. J. Candès, M. Bogdan et al., Controlling the Rate of GWAS False Discoveries, Genetics 205, vol.1, pp.61-75, 2017.

W. S. Bush and J. H. Moore, Chapter 11: Genome-Wide Association Studies, PLoS Comput Biol, vol.8, p.1002822, 2012.

C. Chatelain, G. Durand, V. Thuillier, and F. Augé, Performance of epistasis detection methods in semi-simulated, GWAS". In: BMC Bioinformatics, vol.19, p.1, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01832976

A. Chatr-aryamontri, B. Breitkreutz, R. Oughtred, L. Boucher, and S. Heinicke, The BioGRID interaction database: 2015 update, Nucleic Acids Res, vol.43, pp.470-478, 2015.

B. Chazelle, R. Rubinfeld, and L. Trevisan, Approximating the minimum spanning tree weight in sublinear time, In: SIAM J. Comput, vol.34, pp.1370-1379, 2005.

K. Gary, Y. Chen, and . Guo, Discovering epistasis in large scale genetic association studies by exploiting graphics cards, Frontiers in Genetics, vol.4, 2013.

L. S. Chen, Insights into Colon Cancer Etiology via a Regularized Approach to Gene Set Analysis of GWAS Data, Am J Hum Genet, vol.86, pp.860-871, 2010.

X. Chen, A two-graph guided multi-task Lasso approach for eQTL mapping, 2012.

J. H. Cho, Identification of novel susceptibility loci for inflammatory bowel disease on chromosomes 1p, 3q, and 4q: Evidence for epistasis between 1p and IBD1, Proceedings of the National Academy of Sciences 95, vol.13, pp.7502-7507, 1998.

H. Chuang, E. Lee, Y. Liu, D. Lee, and T. Ideker, Networkbased classification of breast cancer metastasis, Mol Syst Biol, vol.3, p.140, 2007.

M. Claesen, J. Davis, F. D. Smet, and B. D. Moor, Assessing binary classifiers using only positive and unlabeled data, 2015.

R. Clarke, The properties of high-dimensional data spaces: implications for exploring gene and protein expression data, Nature Reviews Cancer, vol.8, pp.37-49, 2008.

-. Héctor-climente and C. Azencott, GWAS incorporating networks in R. 2017

C. Héctor-climente-gonzález, S. Azencott, M. Kaski, and . Yamada, Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data, Bioinformatics, vol.35, 2019.

-. Héctor-climente and A. Chloé-agathe, R package for networkguided Genome-Wide Association Studies, 25th Conference on Intelligent Systems for Molecular Biology (poster), 2017.

L. Héctor-climente-gonzález, L. Christine, S. Fabienne, A. Dominique, A. Nadine et al., Judging genetic loci by the company they keep: Comparing network-based methods for biomarker discovery in familial breast cancer, 68th Annual Meeting of the American Society of Human Genetics (poster), 2018.

O. Combarros, M. Cortina-borja, A. D. Smith, and D. J. Lehmann, Epistasis in sporadic Alzheimer's disease, Neurobiology of Aging, vol.30, pp.1333-1349, 2009.

K. N. Conneely and M. Boehnke, So many correlated tests, so little time! Rapid adjustment of P values for multiple correlated tests, Am. J. Hum. Genet, vol.81, pp.1158-1168, 2007.

H. J. Cordell, Detecting gene-gene interactions that underlie human diseases, Nature Reviews Genetics, vol.10, pp.392-404, 2009.

H. J. Cordell, G. C. Wedig, K. B. Jacobs, and R. C. Elston, Multilocus Linkage Tests Based on Affected Relative Pairs, The American Journal of Human Genetics, vol.66, pp.1273-1286, 2000.

M. Thomas, J. A. Cover, and . Thomas, Elements of Information Theory. 2nd, 2006.

L. Cowen, T. Ideker, B. J. Raphael, and R. Sharan, Network propagation: a universal amplifier of genetic associations, Nature Reviews Genetics, vol.18, pp.551-562, 2017.

N. J. Cox, Loci on chromosomes 2 ( NIDDM1 ) and 15 interact to increase susceptibility to diabetes in Mexican Americans, Nature Genetics, vol.21, pp.213-215, 1999.

M. Defferrard, X. Bresson, and P. Vandergheynst, Convolutional neural networks on graphs with fast localized spectral filtering, Advances in neural information processing systems, pp.3844-3852, 2016.

D. Dernoncourt, B. Hanczar, and J. Zucker, Analysis of feature selection stability on high dimension and small sample data, Computational Statistics & Data Analysis, vol.71, pp.681-693, 2014.

C. Ding and H. Peng, Minimum Redundancy Feature Selection from Microarray Gene Expression Data, Journal of Bioinformatics and Computational Biology, pp.185-205, 2005.

A. Drouin, G. Letarte, F. Raymond, M. Marchand, J. Corbeil et al., Interpretable genotype-to-phenotype classifiers with performance guarantees, Scientific Reports, vol.9, p.4071, 2019.

J. L. Durant, B. A. Leland, D. R. Henry, and J. G. Nourse, Reoptimization of MDL Keys for Use in Drug Discovery, Journal of Chemical Information and Computer Sciences, vol.42, issue.6, pp.1273-1280, 2002.

D. Duroux, Improving efficiency in epistasis detection with a gene-based analysis using functional filters". 28th International Genetic Epidemiology Society meeting (poster), 2019.

S. Ecker, V. Pancaldi, D. Rico, and A. Valencia, Higher gene expression variability in the more aggressive subtype of chronic lymphocytic leukemia, Genome Med, vol.7, issue.1, p.8, 2015.

F. Eduati, Opportunities and limitations in the prediction of population responses to toxic compounds assessed through a collaborative competition, Nature Biotechnology, vol.33, pp.933-940, 2015.

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, The Annals of statistics, vol.32, pp.407-499, 2004.

I. Liat-ein-dor, G. Kela, D. Getz, E. Givol, and . Domany, Outcome signature genes in breast cancer: is there a unique set?, In: Bioinformatics, vol.21, pp.171-178, 2005.

D. Erhan, P. Heureux, Y. Shi, Y. Yue, and . Bengio, Collaborative filtering on a family of biological targets, Journal of chemical information and modeling, vol.46, issue.2, pp.626-635, 2006.

G. Sinan-erten, M. Bebek, and . Koyutürk, Vavien: an algorithm for prioritizing candidate disease genes based on topological similarity of proteins in interaction networks, Journal of Computational Biology: A Journal of Computational Molecular Cell Biology, vol.18, pp.1561-1574, 2011.

T. Evgeniou, A. Charles, M. Micchelli, and . Pontil, Learning multiple tasks with kernel methods, J Mach Learn Res, pp.615-637, 2005.

P. Fardin, A. Barla, S. Mosci, L. Rosasco, A. Verri et al., The l1-l2 regularization framework unmasks the hypoxia signature hidden in the transcriptome of a set of heterogeneous neuroblastoma cell lines, BMC Genomics, vol.10, p.474, 2009.

J. Faulon, M. Misra, S. Martin, K. Sale, and R. Sapra, Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor, Bioinformatics, vol.24, pp.225-233, 2008.

H. Fei and J. Huan, Structured feature selection and task relationship inference for multi-task learning, Knowledge and Information Systems, vol.35, pp.345-364, 2013.

A. Fischer and A. Rausell, Primary immunodeficiencies suggest redundancy within the human immune system, Science immunology, vol.1, issue.6, p.5861, 2016.

R. A. Fisher, XV.-The Correlation between Relatives on the Supposition of Mendelian Inheritance, Transactions of the Royal Society of Edinburgh, vol.52, pp.399-433, 1919.

L. I. Furlong, Human diseases through the lens of network biology, Trends in Genetics, vol.29, pp.150-159, 2013.

G. Gallo, M. D. Grigoriadis, and R. E. Tarjan, A fast parametric maximum flow algorithm and applications, SIAM Journal on Computing, vol.18, pp.30-55, 1989.

E. Geuze, E. Vermetten, and J. D. Bremner, MR-based in vivo hippocampal volumetrics: 1. Review of methodologies currently employed, Molecular Psychiatry, vol.10, pp.147-159, 2005.

J. Gillis and P. Pavlidis, Guilt by association" is the exception rather than the rule in gene networks, PLoS computational biology, vol.8, issue.3, p.1002444, 2012.

V. Gligorijevi?, N. Malod-dognin, and N. Pr?ulj, Integrative methods for analyzing big data in precision medicine, Proteomics, vol.16, pp.741-758, 2016.

K. Goh, M. E. Cusick, D. Valle, B. Childs, M. Vidal et al., The human disease network, Proceedings of the National Academy of Sciences, vol.104, pp.8685-8690, 2007.

M. Gönen, Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization, Bioinformatics, vol.28, pp.2304-2310, 2012.

B. Goudey, GWIS -model-free, fast and exhaustive search for epistatic interactions in case-control GWAS, BMC Genomics, vol.14, p.10, 2013.

D. M. Greig, B. T. Porteous, and A. H. Seheult, Exact maximum a posteriori estimation for binary images, J. R. Stat. Soc, vol.51, issue.2, 1989.

A. Gretton, O. Bousquet, A. Smola, and B. Schölkopf, Measuring statistical dependence with Hilbert-Schmidt norms, 2005.

A. Gretton, K. Fukumizu, H. Choon, L. Teo, B. Song et al., A Kernel Statistical Test of Independence, Advances in Neural Information Processing Systems 20, pp.585-592, 2008.

A. Gretton, Kernel Constrained Covariance for Dependence Measurement, Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, pp.1-8, 2005.

D. Grimm, The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity, Human Mutation, vol.36, issue.5, pp.513-523, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01246688

A. Grover and J. Leskovec, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining -KDD '16, pp.855-864, 2016.

D. Guala and E. L. Sonnhammer, A large-scale benchmark of gene prioritization methods, Scientific reports, vol.7, p.46598, 2017.

. Daniel-f-gudbjartsson, Many sequence variants affecting diversity of adult human height, Nature Genetics, vol.40, pp.609-615, 2008.

C. Anja, D. Gumpinger, . Roqueiro, G. Dominik, K. M. Grimm et al., Methods and Tools in Genome-wide Association Studies, Computational Cell Biology, pp.93-136, 2018.

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, J. Mach. Learn. Res, vol.3, pp.1157-1182, 2003.

W. Hamilton, Z. Ying, and J. Leskovec, Inductive representation learning on large graphs, Advances in Neural Information Processing Systems, pp.1024-1034, 2017.

W. L. Hamilton, R. Ying, and J. Leskovec, Representation Learning on Graphs: Methods and Applications, 2017.

T. Hastie, S. Rosset, R. Tibshirani, and J. Zhu, The entire regularization path for the support vector machine, Journal of Machine Learning Research, vol.5, pp.1391-1415, 2005.

T. Hastie, R. Tibshirani, and M. Wainwright, Statistical Learning with Sparsity: The Lasso and Generalizations, 2015.

A. C. Haury, F. Mordelet, P. Vera-licona, and J. P. Vert, TIGRESS: Trustful Inference of Gene REgulation using Stability Selection, BMC Systems Biology, vol.6, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00694218

A. Haury, P. Gestraud, and J. Vert, The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures, PLoS ONE 6, vol.12, p.28210, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00559580

D. Heckerman, C. Kadie, and J. Listgarten, Leveraging information across HLA alleles/supertypes improves epitope prediction, J Comput Biol, vol.14, pp.736-746, 2007.

O. Hellevik, Linear versus logistic regression when the dependent variable is a dichotomy, Quality & Quantity, vol.43, pp.59-74, 2009.

G. Hemani, A. Theocharidis, W. Wei, and C. Haley, EpiGPU: exhaustive pairwise epistasis scans parallelized on consumer level graphics cards, Bioinformatics, vol.27, pp.1462-1465, 2011.

J. W. Ho, M. Stefani, G. Cristobaldo, M. A. Dos-remedios, and . Charleston, Differential variability analysis of gene expression and its application to human diseases, Bioinformatics, vol.24, pp.390-398, 2008.

L. A. Hothorn, O. Libiger, and D. Gerhard, Model-specific tests on variance heterogeneity for detection of potentially interacting genetic loci, BMC Genet, vol.13, p.59, 2012.

X. Hu, SHEsisEpi, a GPU-enhanced genome-wide SNP-SNP interaction scanning algorithm, efficiently reveals the risk genetic epistasis in bipolar disorder, Cell Research, vol.20, pp.854-857, 2010.

J. Huang, T. Zhang, and D. Metaxas, Learning with structured sparsity, J. Mach. Learn. Res, vol.12, pp.3371-3412, 2011.

T. Ideker, O. Ozier, B. Schwikowski, and A. F. Siegel, Discovering regulatory and signalling circuits in molecular interaction networks, Bioinformatics 18.suppl, pp.233-240, 2002.

L. Jacob, G. Obozinski, and J. Vert, Group lasso with overlap and graph lasso, Proceedings of the 26th Annual International Conference on Machine Learning, pp.433-440, 2009.

L. Jacob and J. Vert, Protein-ligand interaction prediction: an improved chemogenomics approach, Bioinformatics, vol.24, pp.2149-2156, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00433572

S. Jain, M. White, and P. Radivojac, Recovering True Classifier Performance in Positive-Unlabeled Learning, 2017.

V. Janji? and N. Pr?ulj, Biological function through network topology: a survey of the human diseasome, Briefings in functional genomics, vol.11, issue.6, pp.522-532, 2012.

P. Jia, Network-Assisted Investigation of Combined Causal Signals from Genome-Wide Association Studies in Schizophrenia, PLoS Comput Biol, vol.8, issue.7, p.1002587, 2012.

C. Stephen and . Johnson, Hierarchical clustering schemes, Psychometrika, vol.32, pp.241-254, 1967.

M. Iain, D. Johnstone, and . Michael-titterington, Statistical challenges of highdimensional data, In: Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, vol.367, pp.4237-53, 1906.

T. Kam-thong, B. Pütz, N. Karbalai, B. Müller-myhsok, and K. Borgwardt, Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs, Bioinformatics, vol.27, pp.214-221, 2011.

T. Kam-thong, EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units, European Journal of Human Genetics, vol.19, pp.465-471, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00598939

T. Kam-thong, GLIDE: GPU-based linear regression for the detection of epistasis, Human Heredity, vol.73, pp.220-236, 2012.

M. A. Kayala, C. Azencott, J. H. Chen, and P. Baldi, Learning to predict chemical reactions, Journal of Chemical Information and Modeling, vol.51, pp.2209-2222, 2011.

S. Kim, K. Sohn, and E. Xing, A multivariate regression approach to association analysis of a quantitative trait network, Bioinformatics, vol.25, p.12, 2009.

G. Kimmel and R. Shamir, A Block-Free Hidden Markov Model for Genotypes and Its Application to Disease Association, Journal of Computational Biology, vol.12, pp.1243-1260, 2005.

N. Thomas, M. Kipf, and . Welling, Semi-Supervised Classification with Graph Convolutional Networks, 2016.

M. Kircher, P. Daniela-m-witten, . Jain, J. Brian, . O'roak et al., A general framework for estimating the relative pathogenicity of human genetic variants, Nature genetics, vol.46, p.310, 2014.

S. Köhler, S. Bauer, D. Horn, and P. N. Robinson, Walking the Interactome for Prioritization of Candidate Disease Genes, The American Journal of Human Genetics, vol.82, pp.949-958, 2008.

M. A. Kohli, The Neuronal Transporter Gene SLC6A15 Confers Risk to Major Depression, Neuron, vol.70, pp.252-265, 2011.

V. Kolmogorov and R. Zabin, What energy functions can be minimized via graph cuts, IEEE Trans. Pattern Anal. Mach. Intell, vol.26, pp.147-159, 2004.

C. Kramer and P. Gedeck, Leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets, Journal of chemical information and modeling, vol.50, pp.1961-1969, 2010.

R. Kuang, Profile-based string kernels for remote homology detection and motif extraction, Journal of bioinformatics and computational biology, vol.3, pp.527-550, 2005.

I. Ludmila and . Kuncheva, A Stability Index for Feature Selection, Proceedings of the 25th Conference on Proceedings of the 25th IASTED International Multi-Conference: Artificial Intelligence and Applications, pp.390-395, 2007.

I. Ludmila, C. J. Kuncheva, Y. Smith, C. O. Syed, K. E. Phillips et al., Evaluation of feature ranking ensembles for high-dimensional biomedical data, ICDM Workshops, pp.49-56, 2012.

I. Kuperstein, Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps

L. Coulter-kwee, D. Liu, X. Lin, D. Ghosh, and M. P. Epstein, A Powerful and Flexible Multilocus Association Test for Quantitative Traits, The American Journal of Human Genetics, vol.82, pp.386-397, 2008.

E. Twan-van-laarhoven and . Marchiori, Predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile, PloS one, vol.8, issue.6, p.66952, 2013.

. Twan-van-laarhoven, B. Sander, E. Nabuurs, and . Marchiori, Gaussian interaction profile kernels for predicting drug-target interaction, Bioinformatics, vol.27, pp.3036-3043, 2011.

V. Law, DrugBank 4.0: shedding new light on drug metabolism, Nucleic acids research, vol.42, pp.1091-1097, 2013.

J. Lazarou, H. Bruce, P. Pomeranz, and . Corey, Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies, Jama, vol.279, pp.1200-1205, 1998.

M. Le-morvan and J. Vert, WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models, Proceedings of the 35th International Conference on Machine Learning. Ed. by Jennifer Dy and Andreas Krause, vol.80, pp.3635-3644, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01711018

J. D. Lee, D. L. Sun, Y. Sun, and J. E. Taylor, Exact post-selection inference, with application to the lasso, The Annals of Statistics, vol.44, pp.907-927, 2016.

S. Lee, R. Gonçalo, M. Abecasis, X. Boehnke, and . Lin, Rare-Variant Association Analysis: Study Designs and Statistical Tests, Am J Hum Genet, vol.95, pp.5-23, 2014.

S. Lee, P. Eric, and . Xing, Leveraging input and output structures for joint mapping of epistatic and marginal eQTLs, Bioinformatics, vol.28, pp.137-146, 2012.

C. Li and H. Li, Network-constrained regularization and variable selection for analysis of genomic data, Bioinformatics, vol.24, pp.1175-1182, 2008.

C. Li and H. Li, Variable selection and regression analysis for graphstructured covariates with an application to genomics, Ann. Appl. Stat, vol.4, issue.3, pp.1498-1516, 2010.

C. Li, Personalized medicine -the promised land: are we there yet?, In: Clinical Genetics, vol.79, pp.403-412, 2011.

J. Li and A. T. Lamere, DiPhiSeq: Robust comparison of expression levels on RNA-Seq data with large sample sizes, Bioinformatics, 2018.

Y. Li and J. Li, Disease gene identification by random walk on multigraphs merging heterogeneous genomic and phenotype data, BMC Genomics, vol.13, p.27, 2012.

D. Liu, D. Ghosh, and X. Lin, Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models, BMC bioinformatics, vol.9, p.292, 2008.

D. Liu, X. Lin, and D. Ghosh, Semiparametric regression of multidimensional genetic pathway data: Least-squares kernel machines and linear mixed models, Biometrics, vol.63, pp.1079-1088, 2007.

J. Z. Liu, A. F. Mcrae, D. R. Nyholt, and S. E. Medland, A Versatile Gene-Based Test for Genome-wide Association Studies, Am J Hum Genet, vol.87, pp.139-145, 2010.

J. Liu, K. Wang, S. Ma, and J. Huang, Accounting for linkage disequilibrium in genome-wide association studies: A penalized regression method, Statistics and Its Interface, vol.6, pp.99-115, 2013.

W. Liu, P. Chen, S. Yeung, T. Suzumura, and L. Chen, Principled Multilayer Network Embedding, 2017.

Y. Liu, M. Wu, C. Miao, P. Zhao, and X. Li, Neighborhood regularized logistic matrix factorization for drug-target interaction prediction, PLoS computational biology, vol.12, p.1004760, 2016.

R. Luke, M. R. Lloyd-jones, J. Robinson, P. M. Yang, and . Visscher, Transformation of Summary Statistics from Linear Mixed Model Association on All-or-None Traits to Odds Ratio, Genetics, vol.208, pp.1397-1408, 2018.

R. Joshua, J. E. Loftus, and . Taylor, Selective inference in regression models with groups of variables, 2015.

L. Lovász, Random Walks on Graphs: A Survey, Combinatorics, vol.2, pp.1-46, 1993.

M. I. Love, W. Huber, and S. Anders, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biol. 15, vol.12, p.550, 2014.

J. K. Lunceford and M. Davidian, Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study, Statistics in Medicine, vol.23, pp.2937-2960, 2004.

P. Mahé, N. Ueda, T. Akutsu, J. Perret, and J. Vert, Graph kernels for molecular structure-activity relationship analysis with support vector machines, Journal of chemical information and modeling, vol.45, pp.939-951, 2005.

N. Malod-dognin, J. Petschnigg, and N. Pr?ulj, Precision medicine -A promising, yet challenging road lies ahead, Current Opinion in Systems Biology, vol.7, pp.1-7, 2018.

T. A. Manolio, Finding the missing heritability of complex diseases, Nature, vol.461, pp.747-753, 2009.

J. C. Mar, Variance of gene expression identifies altered network constraints in neurological disease, PLoS Genet. 7, vol.8, p.1002207, 2011.

D. Marbach, D. Lamparter, G. Quon, M. Kellis, Z. Kutalik et al., Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases, Nature methods, vol.13, p.366, 2016.

M. Massias, A. Gramfort, and J. Salmon, Celer: a Fast Solver for the Lasso with Dual Extrapolation, ICML 2018 -35th International Conference on Machine Learning, vol.80, pp.3321-3330, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01833398

M. T. Maurano, Systematic localization of common disease-associated variation in regulatory DNA, Science, vol.337, pp.1190-1195, 2012.

J. Mei, C. Kwoh, P. Yang, X. Li, and J. Zheng, Drugtarget interaction prediction by learning from local information and neighbors, Bioinformatics, vol.29, pp.238-245, 2013.

N. Meinshausen and P. Bühlmann, Stability selection, J. R. Stat. Soc, vol.72, pp.417-473, 2010.

R. Merris, Laplacian matrices of graphs: a survey, Linear Algebra Appl, vol.197, pp.143-176, 1994.

C. A. Micchelli, J. M. Morales, and M. Pontil, Regularizers for structured sparsity, Adv. Comput. Math, vol.38, pp.455-489, 2013.

V. Michel, A. Gramfort, G. Varoquaux, E. Eger, and B. Thirion, Total variation regularization for fMRI-based prediction of behavior, IEEE transactions on medical imaging, vol.30, pp.1328-1340, 2011.

T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient Estimation of Word Representations in Vector Space, 2013.

K. Mitra, A. Carvunis, S. Kumar-ramesh, and T. Ideker, Integrative approaches for finding modular structure in biological networks, Nat Rev Genet, vol.14, pp.719-732, 2013.

J. H. Moore, A global view of epistasis, Nature Genetics, vol.37, pp.13-14, 2005.

J. H. Moore, F. W. Asselbergs, and S. M. Williams, Bioinformatics challenges for genome-wide association studies, Bioinformatics, p.713, 2010.

F. Mordelet and J. Vert, A bagging SVM to learn from positive and unlabeled examples, Pattern Recognition Letters, vol.37, pp.201-209, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01101852

F. Mordelet and J. Vert, ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples, BMC Bioinformatics, vol.12, p.389, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00594774

J. Mullenbach, S. Wiegreffe, J. Duke, J. Sun, and J. Eisenstein, Explainable Prediction of Medical Codes from Clinical Text, pp.1101-1111, 2018.

M. Preethy-sasidharan-nair and . Vihinen, VariBench: a benchmark database for variations, Human mutation, vol.34, pp.42-49, 2013.

S. Nakagawa, A farewell to Bonferroni: the problems of low statistical power and publication bias, Behavioral Ecology, vol.15, pp.1044-1045, 2004.

F. Napolitano, Drug repositioning: a machine-learning approach through data integration, In: J. Cheminformatics, vol.5, p.30, 2013.

E. Ndiaye, O. Fercoq, A. Gramfort, and J. Salmon, Gap safe screening rules for sparsity enforcing penalties, The Journal of Machine Learning Research, vol.18, pp.4671-4703, 2017.

Y. Nesterov, Introductory Lectures on Convex Optimization, vol.87, 2004.

P. C. Ng and S. Henikoff, SIFT: Predicting amino acid changes that affect protein function, Nucleic acids research, vol.31, pp.3812-3814, 2003.

C. Niel, C. Sinoquet, C. Dina, and G. Rocheleau, A survey about methods dedicated to epistasis detection, Bioinformatics and Computational Biology, p.285, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01205577

R. Nilsson, J. M. Peña, J. Björkegren, and J. Tegnér, Consistent Feature Selection for Pattern Recognition in Polynomial Time, Journal of Machine Learning Research, vol.8, pp.589-612, 2007.

S. Nogueira and G. Brown, Measuring the stability of feature selection, Machine Learning and Knowledge Discovery in Databases, vol.9852, pp.442-457, 2016.

G. Obozinski, B. Taskar, and M. I. Jordan, Multi-task feature selection, 2006.

J. Igho, C. J. Onakpoya, J. K. Heneghan, and . Aronson, Post-marketing withdrawal of 462 medicinal products because of adverse drug reactions: a systematic review of the world literature, BMC medicine, vol.14, p.10, 2016.

J. B. Orlin, A faster strongly polynomial time algorithm for submodular function minimization, Math. Program, vol.118, pp.237-251, 2009.

M. Oti, B. Snel, A. Martijn, H. Huynen, and . Brunner, Predicting disease genes using protein-protein interactions, Journal of medical genetics, vol.43, issue.8, pp.691-698, 2006.

K. Oualkacha, Adjusted sequence kernel association test for rare variants controlling for cryptic and family relatedness, Genet. Epidemiol, vol.37, pp.366-376, 2013.

T. Pahikkala, Toward more realistic drug-target interaction predictions, Briefings in bioinformatics, p.10, 2014.

G. M. Peloso and K. L. Lunetta, Choice of population structure informative principal components for adjustment in a case-control study, BMC genetics, vol.12, p.64, 2011.

H. Peng, F. Long, and C. Ding, Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, pp.1226-1237, 2005.

S. Jiska, R. M. Peper, D. I. Brouwer, R. S. Boomsma, H. E. Kahn et al., Genetic influences on human brain structure: A review of brain imaging studies in twins, Human Brain Mapping, vol.28, pp.464-473, 2007.

B. Perozzi, R. Al-rfou, and S. Skiena, DeepWalk: Online Learning of Social Representations, Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining -KDD '14, pp.701-710, 2014.

B. Peters, H. Bui, S. Frankild, and M. Nielson, A community resource benchmarking predictions of peptide binding to MHC-I molecules, PLoS Comput Biol, vol.2, issue.6, p.65, 2006.

. Benoit-playe, Machine learning approaches for drug virtual screening, 2019.

C. Benoît-playe, V. Azencott, and . Stoven, Efficient multitask chemogenomics for drug specificity prediction, PLoS ONE, vol.13, p.204999, 2018.

A. L. Price, N. A. Zaitlen, D. Reich, and N. Patterson, New approaches to population stratification in genome-wide association studies, Nature Reviews Genetics, vol.11, issue.7, p.459, 2010.

C. Le-priol, C. Azencott, and X. Gidrol, Large-scale RNAseq datasets enable the detection of genes with a differential expression dispersion in cancer, Computer Science and Mathematics (poster), 2019.

F. Privé, H. Aschard, A. Ziyatdinov, and M. G. Blum, Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr, In: Bioinformatics, 2018.

S. Purcell, PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses, The American Journal of Human Genetics, vol.81, pp.559-575, 2007.

L. Qu, T. Guennel, and S. L. Marshall, Linear score tests for variance components in linear mixed models and applications to genetic association studies, Biometrics, vol.69, pp.883-892, 2013.

L. Ralaivola, J. Sanjay, H. Swamidass, P. Saigo, and . Baldi, Graph kernels for chemical informatics, Neural Networks, vol.18, pp.1093-1110, 2005.

D. Ran and Z. Daye, Gene expression variability and the analysis of largescale RNA-seq studies with the MDSeq, Nucleic Acids Res, vol.45, p.127, 2017.

P. Rastas, M. Koivisto, H. Mannila, and E. Ukkonen, A Hidden Markov Technique for Haplotype Reconstruction, Lecture Notes in Computer Science, pp.140-151, 2005.

S. Reid, J. Taylor, and R. Tibshirani, A General Framework for Estimation and Inference From Clusters of Features, Journal of the American Statistical Association, vol.113, pp.280-293, 2017.

M. D. Ritchie and K. Van-steen, The search for gene-gene interactions in genome-wide association studies: challenges in abundance of methods, practical considerations, and biological interpretation, Annals of translational medicine, vol.6, 2018.

M. D. Ritchie, Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer, The American Journal of Human Genetics, vol.69, pp.138-147, 2001.

M. D. Robinson, D. J. Mccarthy, and G. K. Smyth, edgeR: a Bioconductor package for differential expression analysis of digital gene expression data, Bioinformatics, vol.26, pp.139-140, 2010.

D. Rogers and M. Hahn, Extended-connectivity fingerprints, Journal of chemical information and modeling, vol.50, pp.742-754, 2010.

D. B. Rubin, Estimating causal effects of treatments in randomized and nonrandomized studies, In: Journal of Educational Psychology, vol.66, pp.688-701, 1974.

A. Saha, Co-expression networks reveal the tissue-specific regulation of transcription and splicing, Genome research, vol.27, pp.1843-1858, 2017.

H. Saigo, J. Vert, N. Ueda, and T. Akutsu, Protein homology detection using string alignment kernels, Bioinformatics, vol.20, pp.1682-1689, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00433587

K. Singh-sandhu, G. Li, H. M. Poh, and Y. Quek, Large-Scale Functional Organization of Long-Range Chromatin Interaction Networks, Cell Rep, vol.2, issue.5, pp.1207-1219, 2012.

P. Scheet and M. Stephens, A fast and flexible statistical model for largescale population genotype data: applications to inferring missing genotypes and haplotypic phase, In: American journal of human genetics, vol.78, issue.4, pp.629-673, 2006.

J. Scheiber, Gaining insight into off-target mediated effects of drug candidates with a comprehensive systems chemical biology analysis, Journal of chemical information and modeling, vol.49, pp.308-317, 2009.

M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van-den, I. Berg et al., Modeling Relational Data with Graph Convolutional Networks, 2017.

B. Schölkopf and A. J. Smola, Learning with Kernels, 2002.

N. J. Schork, Personalized medicine: Time for one-person trials, Nature News, vol.520, p.609, 2015.

T. Schüpbach, I. Xenarios, S. Bergmann, and K. Kapur, FastEpistasis: a high performance computing solution for quantitative trait epistasis, Bioinformatics, vol.26, pp.1468-1469, 2010.

D. Rajen, R. J. Shah, and . Samworth, Variable selection with error control: another look at stability selection, Journal of the Royal Statistical Society, vol.75, pp.55-80, 2013.

O. Shchur, M. Mumme, A. Bojchevski, and S. Günnemann, Pitfalls of Graph Neural Network Evaluation, 2018.

N. Sheikh, Z. Kefato, and A. Montresor, gat2vec: representation learning for attributed graphs, In: Computing, pp.1-23, 2018.

K. Solveig and . Sieberts, Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis, Nature Communications, vol.7, p.12460, 2016.

K. Silventoinen, Heritability of adult body height: a comparative study of twin cohorts in eight countries, Twin Research and Human Genetics, vol.6, pp.399-408, 2003.

M. Silver and G. Montana, Fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps, Stat Appl Genet Mol Biol, vol.11, issue.1, p.7, 2012.

L. Slim, C. Chatelain, C. Azencott, and J. Vert, kernelPSI: Post-Selection Inference for Nonlinear Variable Selection, 2010.

L. Slim, C. Chatelain, C. Azencott, and J. Vert, epiGWAS: Robust Methods for Epistasis Detection, 2018.

L. Slim, C. Chatelain, C. Azencott, and J. Vert, Novel methods for epistasis detection in genome-wide association studies, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01984919

L. Slim, C. Chatelain, C. Azencott, and J. Vert, kernelPSI: a post-selection inference framework for nonlinear variable selection, Proceedings of the Thirty-Sixth International Conference on Machine Learning (ICML), vol.97, pp.5857-5865, 2019.

A. J. Smola and R. Kondor, Kernels and regularization on graphs, Learning Theory and Kernel Machines, vol.2777, pp.144-158, 2003.

A. Sokolov, D. E. Carlin, E. O. Paull, R. Baertsch, and J. M. Stuart, Pathway-Based Genomics Prediction using Generalized Elastic Net, PLoS Comput Biol, vol.12, p.1004790, 2016.

L. Song, A. Smola, A. Gretton, K. M. Borgwardt, and J. Bedo, Supervised feature selection via dependence estimation, Proceedings of the 24th international conference on Machine learning -ICML '07, 2007.

L. Song, A. Smola, A. Gretton, J. Bedo, and K. M. Borgwardt, Feature selection via dependence maximization, JMLR 13, pp.1393-1434, 2012.

J. Hoon-sul, L. S. Martin, and E. Eskin, Population structure in genetic studies: Confounding factors and mixed models, PLoS genetics 14, vol.12, p.1007309, 2018.

S. Sun, M. T. Celia, R. M. Greenwood, and . Neal, Haplotype inference using a Bayesian Hidden Markov model, Genetic Epidemiology, vol.31, pp.937-948, 2007.

S. , J. Swamidass, C. Azencott, K. Daily, and P. Baldi, A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval, Bioinformatics, vol.26, pp.1348-1356, 2010.

S. , J. Swamidass, C. Azencott, T. Lin, H. Gramajo et al., The Influence Relevance Voter: an accurate and interpretable virtual high throughput screening method, Journal of Chemical Information and Modeling, vol.49, pp.756-766, 2009.

S. , J. Swamidass, J. Chen, J. Bruand, P. Phung et al., Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity, Bioinformatics 21.suppl, pp.359-368, 2005.

G. Swirszcz and A. C. Lozano, Multi-level Lasso for Sparse Multitask Regression, Proceedings of the 29th International Conference on Machine Learning (ICML-12, pp.361-368, 2012.

D. Szklarczyk, STRING v10: protein-protein interaction networks, integrated over the tree of life, Nucleic Acids Research, vol.43, pp.447-452, 2015.

H. Tan, Epistasis between catechol-O-methyltransferase and type II metabotropic glutamate receptor 3 genes on working memory brain function, Proceedings of the National Academy of Sciences, vol.104, pp.12536-12541, 2007.

G. Murat-ta?an, T. Musso, M. Hao, C. A. Vidal, F. P. Macrae et al., Selecting causal genes from genome-wide association studies via functionally coherent subnetworks, Nat Methods, vol.12, pp.154-159, 2015.

M. A. Taub, R. Holger, S. G. Schwender, T. A. Younkin, I. Louis et al., On multi-marker tests for association in case-control studies, In: Front. Genet, vol.4, p.252, 2013.

, Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls, Nature, vol.447, pp.661-678, 2007.

T. Thornton, Statistical Methods for Genome-Wide and Sequencing Association Studies of Complex Traits in Related Samples, Curr Protoc Hum Genet, vol.84, 2015.

L. Tian, A. A. Alizadeh, A. J. Gentles, and R. Tibshirani, A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates, Journal of the American Statistical Association, vol.109, pp.1517-1532, 2014.

R. Tibshirani, Regression shrinkage and selection via the Lasso, J. R. Stat. Soc, vol.58, pp.267-288, 1994.

R. Tibshirani, M. Saunders, S. Rosset, J. Zhu, and K. Knight, Sparsity and smoothness via the fused lasso, J Roy Stat Soc B, vol.67, pp.91-108, 2005.

R. Todeschini and V. Consonni, Handbook of molecular descriptors, vol.11, 2008.

I. Tur, A. Roverato, and R. Castelo, Mapping eQTL Networks with mixed Graphical Markov Models, Genetics, vol.198, pp.1377-1393, 2014.

A. Valdeolivas, Random walk with restart on multiplex and heterogeneous biological networks, Bioinformatics, vol.35, pp.497-505, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01946427

M. Eliezer, N. Van-allen, M. A. Wagle, and . Levy, Clinical analysis and interpretation of cancer genome data, J. Clin. Oncol. 31, vol.15, pp.1825-1833, 2013.

O. Vanunu, O. Magger, E. Ruppin, T. Shlomi, and R. Sharan, Associating genes and protein complexes with disease via network propagation, PLoS computational biology, vol.6, p.1000641, 2010.

J. , P. Vert, and L. Jacob, Machine learning for in silico virtual screening and chemical genomics: new strategies, Combinatorial chemistry & high throughput screening, vol.11, pp.677-685, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00433569

M. Peter and . Visscher, 10 years of GWAS discovery: biology, function, and translation, The American Journal of Human Genetics, vol.101, issue.1, pp.5-22, 2017.

J. Walters, -. Williams, and Y. Li, Estimation of Mutual Information: A Survey, Rough Sets and Knowledge Technology, pp.389-396, 2009.

X. Wan, BOOST: A Fast Approach to Detecting Gene-Gene Interactions in Genome-wide Case-Control Studies, The American Journal of Human Genetics, vol.87, pp.325-340, 2010.

B. Wang, Similarity network fusion for aggregating data types on a genomic scale, Nature Methods, vol.11, issue.3, pp.333-337, 2014.

J. Wang, Y. Chen, S. Hao, X. Peng, and L. Hu, Deep learning for sensor-based activity recognition: A survey, Pattern Recognition Letters. Deep Learning for Pattern Recognition, vol.119, pp.3-11, 2019.

L. Wang, T. Matsushita, L. Madireddy, P. Mousavi, and S. E. Baranzini, PINBPA: Cytoscape app for network analysis of GWAS data, Bioinformatics, vol.31, pp.262-264, 2015.

R. Wang, Y. Lu, and S. Wang, Comparative Evaluation of 11 Scoring Functions for Molecular Docking, Journal of Medicinal Chemistry, vol.46, pp.2287-2303, 2003.

Z. Wang, T. Liu, Z. Lin, J. Hegarty, W. A. Koltun et al., A general model for multilocus epistatic interactions in case-control studies, PLoS ONE, vol.5, p.11384, 2010.

Z. Wang and G. Montana, The Graph-Guided Group Lasso for Genome-Wide Association Studies, Regularization, Optimization, Kernels, and Support Vector Machines, pp.131-157, 2014.

K. Watanabe, E. Taskesen, A. Van-bochoven, and D. Posthuma, Functional mapping and annotation of genetic associations with FUMA, Nature communications, vol.8, issue.1, p.1826, 2017.

C. Widmer, Further improvements to linear mixed models for genomewide association studies, Scientific reports, vol.4, p.6874, 2014.

M. Scott, M. D. Williams, J. A. Ritchie, I. Phillips, and E. Dawson, Multilocus Analysis of Hypertension: A Hierarchical Approach, Hum Hered, vol.57, pp.28-38, 2004.

M. James-d-wilson, R. Baybay, P. Sankar, and . Stillman, Fast embedding of multilayer networks: An algorithm and application to group fMRI, 2018.

C. Michael, S. Wu, T. Lee, Y. Cai, M. Li et al., Rare-variant association testing for sequencing data with the Sequence Kernel Association Test, Am. J. Hum. Genet, vol.89, pp.82-93, 2011.

Y. F. Tong-tong-wu, T. Chen, E. Hastie, K. Sobel, and . Lange, Genome-wide association analysis by lasso penalized logistic regression, Bioinformatics, vol.25, pp.714-721, 2009.

Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang et al., A comprehensive survey on graph neural networks". In: arXiv preprint, 2019.

Z. Xia, L. Wu, X. Zhou, . Stephen, and . Wong, Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces, BMC systems biology 4.Suppl, p.6, 2010.

B. Xin, Y. Kawahara, Y. Wang, and W. Gao, Efficient Generalized Fused Lasso and its Application to the Diagnosis of Alzheimer's Disease, Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014.

M. Yamada, W. Jitkrittum, L. Sigal, E. P. Xing, and M. Sugiyama, High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso, Neural computation, vol.26, pp.185-207, 2014.

M. Yamada, Y. Umezu, K. Fukumizu, and I. Takeuchi, Post Selection Inference with Kernels, Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, vol.84, pp.152-160, 2018.

M. Yamada, Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data, IEEE Transactions on Knowledge and Data Engineering, vol.30, pp.1352-1365, 2018.

Y. Yamanishi, M. Araki, A. Gutteridge, W. Honda, and M. Kanehisa, Prediction of drug-target interaction networks from the integration of chemical and genomic spaces, Bioinformatics, vol.24, pp.232-240, 2008.

F. Yang, R. F. Barber, P. Jain, and J. Lafferty, Selective inference for group-sparse linear models, Advances in Neural Information Processing Systems, pp.2469-2477, 2016.

J. Yang, Common SNPs explain a large proportion of the heritability for human height, Nature genetics, vol.42, p.565, 2010.

S. Yang, L. Yuan, Y. Lai, and X. Shen, Feature grouping and selection over an undirected graph, Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, pp.922-930, 2012.

Y. Ling-sing, C. Yang, X. Wan, and W. Yu, GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies, Bioinformatics, vol.27, pp.1309-1310, 2011.

F. Zhang, Increased Variability of Genomic Transcription in Schizophrenia, In: Sci Rep, vol.5, p.17995, 2015.

H. Zhang, J. Shi, F. Liang, W. Wheeler, R. Stolzenberg-solomon et al., A fast multilocus test with adaptive SNP selection for largescale genetic-association studies, Eur. J. Hum. Genet, vol.22, pp.696-702, 2014.

Q. Zhang, S. Filippi, A. Gretton, and D. Sejdinovic, Large-scale kernel methods for independence testing, Statistics and Computing, vol.28, pp.113-130, 2018.

Y. Zhang, D. Yeung, and Q. Xu, Probabilistic multi-task feature selection, NIPS. 2010, pp.2559-2567

J. Zhao, S. Gupta, M. Seielstad, J. Liu, and A. Thalamuthu, Pathway-based analysis using reduced gene subsets in genome-wide association studies, BMC Bioinformatics, vol.12, p.17, 2011.

X. Zheng, H. Ding, H. Mamitsuka, and S. Zhu, Collaborative matrix factorization with multiple similarities for predicting drug-target interactions, Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.1025-1033, 2013.

Y. Zhou, R. Jin, and S. Hoi, Exclusive Lasso for multi-task feature selection, J. Mach. Learn. Res, vol.9, pp.989-995, 2010.

M. Zitnik and J. Leskovec, Predicting multicellular function through multi-layer tissue networks, Bioinformatics, vol.33, pp.190-198, 2017.

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.67, pp.301-320, 2005.

O. Zuk, E. Hechter, R. Shamil, E. S. Sunyaev, and . Lander, The mystery of missing heritability: Genetic interactions create phantom heritability, Proceedings of the National Academy of Sciences of the United States of America, vol.109, pp.1193-1201, 2012.