L. B. Alexandrov, S. Nik-zainal, D. C. Wedge, S. A. Aparicio, S. Behjati et al.,

N. Bignell, A. Bolli, and . Borg, Signatures of mutational processes in human cancer, Nature, vol.500, issue.7463, pp.415-421, 2013.

D. Amaratunga and J. Cabrera, Analysis of Data From Viral DNA Microchips, J. Am. Stat. Assoc, vol.96, issue.456, p.61, 2001.

S. A. Andres, G. N. Brock, and J. L. Wittliff, Interrogating differences in expression of targeted gene sets to predict breast cancer outcome, BMC Cancer, vol.13, issue.1, p.41, 2013.

S. L. Anzick, AIB1, a Steroid Receptor Coactivator Amplified in Breast and Ovarian Cancer. Science (80-. ), vol.277, p.41, 1997.

N. Aronszajn, Theory of Reproducing Kernels, Trans. Am. Math. Soc, vol.68, issue.3, p.17, 1950.

D. Arthur and S. Vassilvitskii, k-means++: The advantages of careful seeding, Proc. 18th Annu. ACM-SIAM Symp. Discret. algorithms, p.56, 2007.

E. A. Ashley, Towards precision medicine, 2016.

P. L. Auer and G. Lettre, Rare variant association studies: Considerations, challenges and opportunities, Genome Med, vol.7, issue.1, p.11, 2015.

S. Babaei, M. Hulsman, M. Reinders, and J. De-ridder, Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion, BMC Bioinformatics, vol.14, issue.1, p.33, 2013.

Y. Bachrach, Y. Finkelstein, R. Gilad-bachrach, L. Katzir, N. Koenigstein et al., Nice and U. Paquet. Speeding up the Xbox recommender system using a euclidean transformation for innerproduct spaces, Proc. 8th ACM Conf. Recomm. Syst, p.95, 2014.

E. Barillot, L. Calzone, P. Hupé, J. Vert, and A. Zinovyev, Computational systems biology of cancer, p.33, 2012.

R. E. Barlow, D. Bartholomew, J. M. Bremner, and H. D. Brunk, Statistical inference under order restrictions; the theory and application of isotonic regression, p.67, 1972.

T. Barrett, D. B. Troup, S. E. Wilhite, P. Ledoux, C. Evangelista et al., NCBI GEO: archive for functional genomics data sets -10 years on, Nucleic Acids Res, vol.39, issue.1, p.71, 2011.

A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM J. Imaging Sci, vol.2, issue.1, p.24, 2009.

R. Bellman, Adaptive control processes: A guided tour, p.14, 1961.

Y. Bengio, A. Courville, and P. Vincent, Representation Learning: A Review and New Perspectives, IEEE Trans. Pattern Anal. Mach. Intell, vol.35, issue.8, p.19, 2013.

Y. Bengio, Y. Mesnil, G. Dauphin, and S. Rifai, Better mixing via deep representations, Proc. 30th Int. Conf. Mach. Learn, p.20, 2013.

P. J. Bickel, Y. Ritov, and A. B. Tsybakov, hierarchical selection of variables in sparse highdimensional regression, Borrow. strength theory powering Appl. Festschrift Lawrence D. Brown, p.78, 2010.

J. Bien, J. Taylor, and R. Tibshirani, A lasso for hierarchical interactions, Ann. Stat, vol.41, issue.3, p.96, 2013.

S. Bilodeau, M. H. Kagey, G. M. Frampton, P. B. Rahl, and R. A. Young, SetDB1 contributes to repression of genes encoding developmental regulators and maintenance of ES cell state, Genes \& Dev, vol.23, issue.21, p.61, 2009.

N. J. Birkbak, B. Kochupurakkal, J. M. Izarzugaza, A. C. Eklund, Y. Li et al., Tumor mutation burden forecasts outcome in ovarian cancer with BRCA1 or BRCA2 mutations, PLoS One, vol.8, issue.11, p.33, 2013.

J. R. Black and S. J. Clark, Age-related macular degeneration: genome-wide association studies to translation, Genet. Med, vol.18, issue.4, pp.283-289, 2015.
DOI : 10.1038/gim.2015.70

URL : https://www.nature.com/articles/gim201570.pdf

B. M. Bolstad, R. A. Irizarry, M. Åstrand, and T. P. Speed, A comparison of normalization methods for high density oligonucleotide array data based on variance and bias, Bioinformatics, vol.19, issue.2, p.62, 2003.

A. Bonnefoy, V. Emiya, L. Ralaivola, and R. , Dynamic Screening: Accelerating First-Order Algorithms for the Lasso and Group-Lasso, IEEE Trans. Signal Process, vol.63, p.77, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01084986

J. H. Bullard, E. Purdom, K. D. Hansen, and S. Dudoit, Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments, BMC Bioinformatics, vol.11, issue.1, p.61, 2010.

E. Bullmore and O. Sporns, Complex brain networks: graph theoretical analysis of structural and functional systems, Nat. Rev. Neurosci, vol.10, issue.3, p.21, 2009.
DOI : 10.1038/nrn2575

B. Carvalho, H. Bengtsson, R. P. Speed, and R. A. Irizarry, Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data, Biostatistics, vol.8, issue.2, p.61, 2007.
DOI : 10.1093/biostatistics/kxl042

URL : https://academic.oup.com/biostatistics/article-pdf/8/2/485/633275/kxl042.pdf

E. G. Cerami, B. E. Gross, E. Demir, I. Rodchenkov, N. Babur et al., Pathway Commons, a web resource for biological pathway data, Nucleic Acids Res, vol.39, p.36, 2010.
DOI : 10.1093/nar/gkq1039

URL : https://academic.oup.com/nar/article-pdf/39/suppl_1/D685/7625356/gkq1039.pdf

P. B. Chapman, A. Hauschild, C. Robert, J. B. Haanen, P. Ascierto et al., Improved Survival with Vemurafenib in Melanoma with BRAF V600E Mutation, N. Engl. J. Med, vol.364, issue.26, pp.2507-2516, 2011.

A. Chatr-aryamontri, R. Oughtred, L. Boucher, J. Rust, C. Chang et al., The BioGRID interaction database: 2017 update, Nucleic Acids Res, vol.45, p.38, 2016.

M. Chidgey and C. Dawson, Desmosomes: a role in cancer?, Br. J. Cancer, vol.96, issue.12, p.41, 2007.

J. Chilès and P. Delfiner, Geostatistics: Modeling Spatial Uncertainty, p.61, 2012.

L. Chin and J. W. Gray, Translating insights from the cancer genome into clinical practice, Nature, vol.452, issue.7187, p.33, 2008.
DOI : 10.1038/nature06914

URL : http://europepmc.org/articles/pmc2730524?pdf=render

A. Cho, J. E. Shim, F. Supek, B. Lehner, and I. Lee, MUFFINN: cancer gene discovery via network analysis of somatic mutation data, Genome Biol, vol.17, issue.1, p.53, 2016.
DOI : 10.1186/s13059-016-0989-x

URL : https://genomebiology.biomedcentral.com/track/pdf/10.1186/s13059-016-0989-x

F. R. Chung, Spectral Graph Theory, 1921.

G. Ciriello, M. L. Miller, B. A. Aksoy, Y. Senbabaoglu, N. Schultz et al., Emerging landscape of oncogenic signatures across human cancers, Nat. Genet, vol.45, issue.10, pp.1127-1133, 2013.

N. Cloonan, A. R. Forrest, G. Kolle, B. B. Gardiner, G. J. Faulkner et al., Stem cell transcriptome profiling via massive-scale mRNA sequencing, Nat. Methods, vol.5, issue.7, p.61, 2008.
DOI : 10.1038/nmeth.1223

E. A. Collisson, J. D. Campbell, A. N. Brooks, A. H. Berger, W. Lee et al., Comprehensive molecular profiling of lung adenocarcinoma, Nature, vol.511, issue.7511, p.48, 2014.

P. Creixell, J. Reimand, S. Haider, G. Wu, T. Shibata et al., Pathway and network analysis of cancer genomes, Nat. Methods, vol.2, issue.3, p.33, 2015.

I. Daubechies, M. Defrise, and C. De-mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Commun. Pure Appl. Math, vol.57, issue.11, p.24, 2004.

H. Davies, G. R. Bignell, C. Cox, P. Stephens, S. Edkins et al., Mutations of the BRAF gene in human cancer, Nature, vol.417, issue.6892, pp.949-954, 2002.

S. Dharanipragada and M. Padmanabhan, A nonlinear unsupervised adaptation technique for speech recognition, Proc. 6th Int. Conf. Spok. Lang. Process, p.61, 2000.

P. Diaconis, Group representations in probability and Statistics, p.65, 1988.

M. Dillies, A. Rau, J. Aubert, C. Hennequet-antier, M. Jeanmougin et al., A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis, Brief. Bioinform, vol.14, issue.6, p.61, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01521274

C. M. Dimitrakopoulos and N. Beerenwinkel, Computational approaches for the identification of cancer genes and pathways, Wiley Interdiscip. Rev. Syst. Biol. Med, vol.9, issue.1, p.1364, 2017.

L. Ding, G. Getz, D. A. Wheeler, E. R. Mardis, M. D. Mclellan et al., Somatic mutations affect key pathways in lung adenocarcinoma, Nature, vol.455, issue.7216, p.48, 2008.

D. L. Donoho and J. Tanner, Observed universality of phase transition in high-dimenisonal geometry, with applications for modern data analysis and signal processing, Philos. Trans. R. Soc. London A Math. Phys. Eng. Sci, vol.367, p.89, 1906.

Y. Drier, M. Sheffer, and E. Domany, Pathway-based personalized analysis of cancer, Proc. Natl. Acad. Sci, vol.110, p.19, 2013.

F. Dudbridge, Power and Predictive Accuracy of Polygenic Risk Scores, PLoS Genet, vol.9, issue.3, p.12, 2013.

R. L. Dusek and L. D. Attardi, Desmosomes: new perpetrators in tumour suppression, Nat. Rev. Cancer, vol.11, issue.5, p.41, 2011.

F. Eduati, L. M. Mangravite, T. Wang, H. Tang, J. C. Bare et al., Prediction of human population responses to toxic compounds by a collaborative competition, Nat. Biotechnol, vol.33, issue.9, pp.933-940, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01428019

B. Efron, T. Hastie, I. Johnstone, R. Tibshirani, H. Ishwaran et al., Least angle regression, Ann. Stat, vol.32, issue.2, pp.407-499, 2004.

L. E. Ghaoui, V. Viallon, and T. Rabbani, Safe feature elimination in sparse supervised learning, Pacific J. Optim, vol.8, issue.4, p.77, 2012.

J. Fan and J. Lv, Sure independence screening for ultrahigh dimensional feature space, J. R. Stat. Soc. Ser. B (Statistical Methodol, vol.70, issue.5, p.25, 2008.

O. Fercoq, A. Gramfort, and J. Salmon, Mind the Duality Gap: Safer Rules for the Lasso, Proc. 32nd Int. Conf. Mach. Learn, p.77, 2015.

M. Fontoura, V. Josifovski, J. Liu, S. Venkatesan, X. Zhu et al., Evaluation Strategies for Top-k Queries over Memory-Resident Inverted Indexes, Proc. VLDB Endow, vol.4, p.85, 2011.

J. Friedman, T. Hastie, H. Höfling, and R. Tibshirani, Pathwise coordinate optimization, Ann. Appl. Stat, vol.1, issue.2, p.24, 2007.
DOI : 10.1214/07-aoas131

URL : https://doi.org/10.1214/07-aoas131

J. Friedman, T. Hastie, and R. Tibshirani, Regularization Paths for Generalized Linear Models via Coordinate Descent, J. Stat. Softw, vol.33, issue.1, p.24, 2010.
DOI : 10.18637/jss.v033.i01

URL : https://www.jstatsoft.org/index.php/jss/article/view/v033i01/v33i01.pdf

Y. Fujiwara, Y. Ida, H. Shiokawa, and S. Iwamura, Fast Lasso Algorithm via Selective Coordinate Descent, Proc. 30th Conf, vol.77, p.90, 2016.

P. A. Futreal, L. Coin, M. Marshall, T. Down, T. Hubbard et al., A census of human cancer genes, Nat. Rev. Cancer, vol.4, issue.3, pp.177-183, 2004.

A. Gionis, P. Indyk, and R. Motwani, Similarity Search in High Dimensions via Hashing, Proc. 25th Int. Conf. Very Large Data Bases, p.95, 1999.

R. C. Gonzalez and R. E. Woods, Digital Image Processing, p.61, 2008.

A. Gonzalez-perez, C. Perez-llamas, J. Deu-pons, D. Tamborero, M. P. Schroeder et al., IntOGen-mutations identifies cancer drivers across tumor types, Nat. Methods, vol.10, issue.11, p.41, 2013.

C. Greenman, P. Stephens, R. Smith, G. L. Dalgliesh, C. Hunter et al., Patterns of somatic mutation in human cancer genomes, Nature, vol.446, issue.7132, p.33, 2007.

D. F. Gudbjartsson, G. B. Walters, G. Thorleifsson, H. Stefansson, B. V. Halldorsson et al., Many sequence variants affecting diversity of adult human height, Nat. Genet, vol.40, issue.5, p.11, 2008.

D. Hanahan and R. A. Weinberg, Hallmarks of cancer: The next generation, Cell, vol.144, issue.5, p.33, 2011.

N. Hao and H. H. Zhang, Interaction Screening for Ultra-High Dimensional Data, J. Am. Stat. Assoc, vol.109, issue.507, p.78, 2014.

A. Haris, D. Witten, and N. Simon, Convex Modeling of Interactions With Strong Heredity, J. Comput. Graph. Stat, vol.25, issue.4, p.77, 2016.

T. Hastie and W. Stuetzle, Principal curves, J. Am. Stat. Assoc, vol.84, issue.406, p.19, 1989.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. Springer series in statistics, p.16, 2001.

G. Hemani, S. Knott, and C. Haley, An Evolutionary Perspective on Epistasis and the Missing Heritability, PLoS Genet, vol.9, issue.2, p.1003295, 2013.

S. C. Hicks and R. A. Irizarry, quantro: a data-driven approach to guide the choice of an appropriate normalization method, Genome Biol, vol.16, issue.1, p.61, 2015.

M. R. Hidalgo, C. Cubuk, A. Amadoz, F. Salavert, J. Carbonell-caballero et al., High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes, Oncotarget, vol.8, issue.3, p.18, 2017.

F. Hilger and H. Ney, Quantile based histogram equalization for noise robust large vocabulary speech recognition, IEEE Trans. Audio. Speech. Lang. Processing, vol.14, issue.3, p.61, 2006.

K. A. Hoadley, C. Yau, D. M. Wolf, A. D. Cherniack, D. Tamborero et al., Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin, Cell, vol.158, issue.4, pp.929-944, 2014.

A. E. Hoerl and R. W. Kennard, Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics, vol.12, issue.1, p.16, 1970.

M. Hofree, J. P. Shen, H. Carter, A. Gross, and T. Ideker, Network-based stratification of tumor mutations, Nat. Methods, vol.10, issue.11, p.55, 2013.

H. Horn, M. S. Lawrence, J. X. Hu, E. Worstell, N. Ilic et al., A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomes. bioRxiv, 2015.

J. P. Hou and J. Ma, DawnRank: discovering personalized driver genes in cancer, Genome Med, vol.6, issue.7, p.33, 2014.

N. Howlader, A. Noone, M. Krapcho, D. Miller, K. Bishop et al., Cancer Statistics Review, p.12, 1975.

D. W. Huang, R. A. Lempicki, and B. T. Sherman, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources, Nat. Protoc, vol.4, issue.1, p.58, 2009.

T. J. Hudson, W. Anderson, A. Aretz, and A. D. Barker, International network of cancer genome projects, Nature, vol.464, issue.7291, p.33, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00868358

R. A. Irizarry, B. Hobbs, F. Collin, Y. D. Beazer-barclay, K. J. Antonellis et al., Exploration, normalization, and summaries of high density oligonucleotide array probe level datas, Biostatistics, vol.4, issue.2, p.71, 2003.

P. Jia and Z. Zhao, VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data, PLoS Comput. Biol, vol.10, issue.2, p.33, 2014.

Y. Jiao and J. Vert, The Kendall and Mallows Kernels for Permutations, Proc. 32nd Int. Conf. Mach. Learn., JMLR:W&CP, p.65, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01279273

Y. Jiao, M. R. Hidalgo, C. Çubuk, A. Amadoz, J. Carbonell-caballero et al., Signaling Pathway Activities Improve Prognosis for Breast Cancer, 2017.

T. Johnson and C. Guestrin, Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization, Proc. 32nd Int. Conf. Mach. Learn, vol.80, p.86, 2015.

T. B. Johnson and C. Guestrin, StingyCD: Safely Avoiding Wasteful Updates in Coordinate Descent, Proc. 34th Int. Conf. Mach. Learn, vol.77, p.91, 2017.

C. Kandoth, M. D. Mclellan, F. Vandin, K. Ye, B. Niu et al., Mutational landscape and significance across 12 major cancer types, Nature, vol.503, issue.7471, p.33, 2013.

M. Kanehisa, Y. Sato, M. Kawashima, M. Furumichi, and M. Tanabe, KEGG as a reference resource for gene and protein annotation, Nucleic Acids Res, vol.44, issue.D1, p.58, 2016.

M. Kasowski, F. Grubert, C. Heffelfinger, M. Hariharan, A. Asabere et al., Variation in transcription factor binding among humans, Science, vol.328, issue.5975, p.61, 2010.

R. J. Klein, C. Zeiss, E. Y. Chew, J. Tsai, R. S. Sackler et al., Complement factor H polymorphism in age-related macular degeneration, Science, vol.308, issue.5720, pp.385-389, 2005.

S. Köhler, S. Bauer, D. Horn, and P. N. Robinson, Walking the Interactome for Prioritization of Candidate Disease Genes, Am. J. Hum. Genet, vol.82, issue.4, p.33, 2008.

M. Kowalski, P. Weiss, A. Gramfort, and S. Anthoine, Accelerating ISTA with an active set strategy, OPT 2011 4th Int, p.80, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00696992

A. Krizhevsky, Learning Multiple Layers of Features from Tiny Images, p.68, 2009.

I. Kuperstein, L. Grieco, D. P. Cohen, D. Thieffry, A. Zinovyev et al., The shortest path is not the one you know: Application of biological network resources in precision oncology research, Mutagenesis, vol.30, issue.2, p.33, 2015.

T. Lahusen, R. T. Henke, B. L. Kagan, A. Wellstein, and A. T. Riegel, The role and regulation of the nuclear receptor co-activator AIB1 in breast cancer, Breast Cancer Res. Treat, vol.116, issue.2, p.41, 2009.

E. S. Lander, L. M. Linton, B. Birren, C. Nusbaum, M. C. Zody et al., Initial sequencing and analysis of the human genome, Nature, vol.409, issue.6822, p.860, 2001.

M. S. Lawrence, P. Stojanov, P. Polak, G. V. Kryukov, K. Cibulskis et al., Mutational heterogeneity in cancer and the search for new cancer-associated genes, Nature, vol.499, issue.7457, p.49, 2013.

M. S. Lawrence, P. Stojanov, C. H. Mermel, J. T. Robinson, L. Garraway et al., Discovery and saturation analysis of cancer genes across 21 tumour types, Nature, vol.505, issue.7484, p.33, 2014.

M. , L. Morvan, and J. P. Vert, Supervised Quantile Normalisation, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01525306

M. , L. Morvan, and J. P. Vert, WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01711018

M. Le-morvan, A. Zinovyev, and J. P. Vert, NetNorM: Capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis, PLoS Comput. Biol, vol.13, issue.6, p.1005573, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01341856

I. Lee, U. M. Blom, P. I. Wang, J. E. Shim, and E. M. Marcotte, Prioritizing candidate disease genes by network-based boosting of genome-wide association data, Genome Res, vol.21, issue.7, p.38, 2011.

J. D. Lee, D. L. Sun, Y. Sun, and J. E. Taylor, Exact post-selection inference, with application to the lasso, Ann. Stat, vol.44, issue.3, p.95, 2016.

M. D. Leiserson, F. Vandin, H. Wu, J. R. Dobson, J. V. Eldridge et al., Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes, Nat. Genet, vol.47, issue.2, p.34, 2014.

G. Lettre, A. U. Jackson, C. Gieger, F. R. Schumacher, S. I. Berndt et al., Identification of ten loci associated with height highlights new biological pathways in human growth, Nat. Genet, vol.40, issue.5, p.11, 2008.

M. Lim and T. Hastie, Learning Interactions via Hierarchical Group-Lasso Regularization, J. Comput. Graph. Stat, vol.24, issue.3, p.77, 2015.

D. G. Lowe, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vis, vol.60, issue.2, p.18, 2004.

B. Maher, Personal genomes: The case of the missing heritability, Nature, vol.456, issue.7218, p.11, 2008.

J. Mairal and Y. Bin, Complexity Analysis of the Lasso Regularization Path, Proc. 29th Int. Conf. Mach. Learn, p.24, 2012.

S. G. Mallat and Z. Zhang, Matching Pursuits With Time-Frequency Dictionaries, IEEE Trans. Signal Process, vol.41, issue.12, pp.3397-3415, 1993.

T. A. Manolio, F. S. Collins, N. J. Cox, D. B. Goldstein, L. A. Hindorff et al., Finding the missing heritability of complex diseases, Nature, vol.461, issue.7265, p.11, 2009.

E. R. Mardis, Genome sequencing and cancer, Curr. Opin. Genet. \& Dev, vol.22, issue.3, p.33, 2012.

E. R. Mardis, DNA sequencing technologies, 2006.

I. Martincorena, K. M. Raine, M. Gerstung, K. J. Dawson, K. Haase et al., Universal Patterns of Selection in Cancer and Somatic Tissues, Cell, vol.171, issue.5, pp.1029-1041, 2017.

I. Martinocorena and P. J. Campbell, Somatic mutation in cancer and normal cells. Science (80-. ), vol.349, pp.1483-1489, 2015.

M. Massias, A. Gramfort, and J. Salmon, From safe screening rules to working sets for faster Lasso-type solvers

M. Massias, A. Gramfort, and J. Salmon, Dual Extrapolation for Faster Lasso Solvers, p.25, 2018.

N. Mavaddat, P. D. Pharoah, K. Michailidou, J. Tyrer, M. N. Brook et al., Prediction of breast cancer risk based on profiling with common genetic variants, J. Natl. Cancer Inst, vol.107, issue.5, p.12, 2015.

T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space, p.20, 2013.

S. Molau, M. Pitz, and H. Ney, Histogram based normalization in the acoustic feature space, Proc. IEEE Work. Autom. Speech Recognit. Underst., Madonna di Campiglio, p.61, 2001.

G. Moldovan and A. D. , How the fanconi anemia pathway guards the genome, Annu. Rev. Genet, vol.43, p.49, 2009.

K. Nakagawa, S. Suzumura, M. Karasuyama, K. Tsuda, and I. Takeuchi, Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining, Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min, vol.78, p.114, 2016.

B. K. Natarajan, Sparse Approximate Solutions to Linear Systems, SIAM J. Comput, vol.24, issue.2, pp.227-234, 1995.

E. Ndiaye, O. Fercoq, A. Gramfort, and J. Salmon, Gap Safe screening rules for sparsity enforcing penalties, J. Mach. Learn. Res, vol.18, issue.128, p.80, 2017.

Y. Nesterov, Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems, SIAM J. Optim, vol.22, issue.2, p.24, 2012.

B. Neyshabur and N. Srebro, On Symmetric and Asymmetric LSHs for Inner Product Search, Proc. 32nd Int. Conf. Mach. Learn, p.95, 2015.

L. G. Nyúl and J. K. Udupa, On standardizing the MR image intensity scale, Magn. Reson. Med, vol.42, issue.6, p.61, 1999.

L. G. Nyúl, J. K. Udupa, and X. Zhang, New variants of a method of MRI scale standardization, IEEE Trans. Med. Imaging, vol.19, issue.2, p.61, 2000.

M. Olivier and P. Taniere, Somatic mutations in cancer prognosis and prediction: lessons from TP53 and EGFR genes, Curr. Opin. Oncol, vol.23, issue.1, p.33, 2011.

M. R. Osborne, B. Presnell, and B. A. Turlach, A new approach to variable selection in least squares problems, IMA J. Numer. Anal, vol.20, issue.3, pp.389-403, 2000.

S. Osher and Y. Li, Coordinate descent optimization for l1 minimization with application to compressed sensing; a greedy algorithm, Inverse Probl. Imaging, vol.3, issue.3, p.24, 2009.

N. Parikh and S. Boyd, Proximal Algorithms. Found. Trends® Optim, vol.1, issue.3, p.24, 2014.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res, vol.12, p.55, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00650905

C. M. Perou, T. Sørile, M. B. Eisen, M. Van-de-rijn, S. S. Jeffrey et al., Molecular portraits of human breast tumours, Nature, vol.406, issue.6797, pp.747-752, 2000.

P. C. Phillips, Epistasis-the essential role of gene interactions in the structure and evolution of genetic systems, Nat. Rev. Genet, vol.9, issue.11, p.12, 2008.

T. S. Prasad, R. Goel, K. Kandasamy, S. Keerthikumar, S. Kumar et al., Human Protein Reference Database-2009 update, Nucleic Acids Res, vol.37, p.38, 2009.

A. L. Price, N. J. Patterson, R. M. Plenge, M. E. Weinblatt, N. A. Shadick et al., Principal components analysis corrects for stratification in genome-wide association studies, Nat. Genet, vol.38, issue.8, pp.904-909, 2006.

J. K. Pritchard, Are Rare Variants Responsible for Susceptibility to Complex Diseases?, Am. J. Hum. Genet, vol.69, issue.1, p.11, 2001.

Y. Qian, S. Besenbacher, T. Mailund, and M. H. Schierup, Identifying disease associated genes by network propagation, BMC Syst. Biol, vol.8, issue.1, p.33, 2014.

J. Qiao, S. Cui, L. Xu, S. Chen, J. Yao et al., Filamin C, a dysregulated protein in cancer revealed by label-free quantitative proteomic analyses of human gastric cancer cells, Oncotarget, vol.6, issue.2, p.41, 2014.

P. Radchenko and G. James, Variable selection using Adaptive Nonlinear Interaction Structures in High dimensions, J. Am. Stat. Assoc, vol.105, issue.492, p.77, 2010.

A. Raj, J. Olbrich, B. Gärtner, B. Schölkopf, and M. Jaggi, Screening Rules for Convex Problems, p.77, 2016.

P. Ranganathan, K. L. Weaver, and A. J. Capobianco, Notch signalling in solid tumours: a little bit of everything but not all the time, Nat. Rev. Cancer, vol.11, issue.5, p.41, 2011.

J. Rangel, S. Torabian, L. Shaikh, M. Nosrati, F. L. Baehner et al., Prognostic significance of nuclear receptor coactivator-3 overexpression in primary cutaneous melanoma, J. Clin. Oncol, vol.24, issue.28, p.41, 2006.

F. Rapaport, A. Zinovyev, M. Dutreix, E. Barillot, and J. Vert, Classification of microarray data using gene networks, BMC Bioinformatics, vol.8, issue.1, p.33, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00433577

B. J. Raphael, J. R. Dobson, L. Oesper, and F. Vandin, Identifying driver mutations in sequenced cancer genomes: Computational approaches to enable precision medicine, Genome Med, vol.6, issue.1, p.94, 2014.

N. A. Rizvi, M. D. Hellmann, A. Snyder, P. Kvistborg, V. Makarov et al., Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science (80-. ), vol.348, p.33, 2015.

A. I. Robles and C. C. Harris, Clinical outcomes and correlates of TP53 mutations and cancer, Cold Spring Harb. Perspect. Biol, vol.2, issue.3, p.38, 2010.

M. H. Roh, O. Makarova, C. J. Liu, K. Shin, S. Lee et al., The Maguk protein, Pals1, functions as an adapter, linking mammalian homologues of crumbs and discs lost, J. Cell Biol, vol.157, issue.1, p.41, 2002.

Y. Samuels and T. Waldman, Oncogenic mutations of PIK3CA in human cancers, Curr. Top. Microbiol. Immunol, vol.347, issue.1, pp.21-41, 2010.

R. Scharpf, R. Irizarry, M. Ritchie, B. Carvalho, and I. Ruczinski, Using the R Package crlmm for Genotyping and Copy Number Estimation, J. Stat. Softw, vol.40, issue.1, p.61, 2011.

J. Serres, Linear Representations of Finite Groups, Graduate Texts in Mathematics, p.65, 1977.

M. Shah, Y. Xiao, N. Subbanna, S. Francis, D. L. Arnold et al., Evaluating intensity normalization on MRIs of human brain with multiple sclerosis, Med. Image Anal, vol.15, issue.2, p.61, 2011.

R. D. Shah, Modelling interactions in high-dimensional data with backtracking, J. Mach. Learn. Res, vol.17, issue.207, p.78, 2016.

R. T. Shinohara, E. M. Sweeney, J. Goldsmith, N. Shiee, F. J. Mateen et al., Statistical normalization techniques for magnetic resonance imaging, Neuroimage (Amst), vol.6, p.61, 2014.

A. Shrivastava and P. Li, Asymmetric LSH (ALSH) for sublinear time maximum inner product search (MIPS), Adv. Neural Inf. Process. Syst, vol.92, p.95, 2014.

A. Shrivastava and P. Li, Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS), Proc. 31st Conf, p.95, 2015.

T. Sorlie, C. M. Perou, R. Tibshirani, T. Aas, S. Geisler et al., Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications, Proc. Natl. Acad. Sci, vol.98, issue.19, pp.10869-10874, 2001.

N. Srebro and A. Shraibman, Rank, Trace-Norm and Max-Norm, Int. Conf. Comput. Learn. Theory, p.65, 2005.

M. R. Stratton, P. J. Campbell, and P. A. , The cancer genome, Nature, vol.458, p.33, 2009.

S. Suzumura, K. Nakagawa, Y. Umezu, K. Tsuda, and I. Takeuchi, Selective Inference for Sparse High-Order Interaction Models, Proc. 34th Int. Conf. Mach. Learn., volume, vol.70, p.95, 2017.

O. Sysoev and O. Burdakov, A smoothed monotonic regression via L2 regularization, vol.64, p.67, 2016.

D. Szklarczyk, A. Franceschini, S. Wyder, K. Forslund, D. Heller et al., STRING v10: Protein-protein interaction networks, integrated over the tree of life, Nucleic Acids Res, vol.43, issue.D1, p.38, 2015.

C. Teflioudi and R. Gemulla, Exact and Approximate Maximum Inner Product Search with LEMP, ACM Trans. Database Syst, vol.42, issue.1, p.95, 2016.

A. Tenesa and C. S. Haley, The heritability of human disease: Estimation, uses and abuses, Nat. Rev. Genet, vol.14, issue.2, pp.139-149, 2013.

A. Auton, L. D. Brooks, R. M. Durbin, E. P. Garrison, H. M. Kang et al., A global reference for human genetic variation, The 1000 Genomes Project Consortium, vol.526, pp.68-74, 2015.

, Comprehensive molecular portraits of human breast tumours, Nature, vol.490, issue.7418, p.33, 2012.

, The Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways, Nature, vol.455, issue.7216, p.33, 2008.

, The Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma, Nature, vol.474, issue.7353, p.33, 2011.

T. Network, J. N. Weinstein, E. A. Collisson, G. B. Mills, K. R. Shaw et al., The Cancer Genome Atlas Pan-Cancer analysis project, Nat. Genet, vol.45, issue.10, p.33, 2013.

, The International HapMap Consortium. The International HapMap Project, Nature, vol.426, issue.6968, pp.789-796, 2003.

, The International HapMap Consortium. A haplotype map of the human genome, Nature, vol.437, issue.7063, p.1299, 2005.

, The Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls, Nature, vol.447, issue.7145, p.661, 2007.

R. Tibshirani, Regression Selection and Shrinkage via the Lasso, J. R. Stat. Soc. Ser. B (Statistical Methodol, vol.58, issue.1, p.77, 1996.

R. Tibshirani, J. Bien, J. Friedman, T. Hastie, N. Simon et al., Strong rules for discarding predictors in lasso-type problems, J. R. Stat. Soc. Ser. B (Statistical Methodol, vol.74, issue.2, p.25, 2012.

R. J. Tibshirani and J. Taylor, Degrees of freedom in lasso problems, Ann. Stat, vol.40, issue.2, p.16, 2012.

A. N. Tikhonov, On the stability of inverse problems, Dokl. Akad. Nauk SSSR, vol.39, issue.5, p.16, 1943.

C. Tomasetti, L. Marchionni, M. A. Nowak, G. Parmigiani, and B. Vogelstein, Only three driver gene mutations are required for the development of lung and colorectal cancers, Proc. Natl. Acad. Sci, vol.112, issue.1, pp.118-123, 2015.

V. Van-belle, K. Pelckmans, J. Suykens, and S. Van-huffel, Support vector machines for survival analysis, Proc. 3rd Int. Conf. Comput. Intell. Med. Healthc, p.55, 2007.

F. Vandin, E. Upfal, and B. J. Raphael, Algorithms for detecting significantly mutated pathways in cancer, J. Comput. Biol, vol.18, issue.3, p.33, 2011.

O. Vanunu, O. Magger, E. Ruppin, T. Shlomi, and R. Sharan, Associating genes and protein complexes with disease via network propagation, PLoS Comput. Biol, vol.6, issue.1, p.33, 2010.

J. C. Venter, M. D. Adams, E. W. Myers, P. W. Li, R. J. Mural et al., The sequence of the human genome, Science, vol.291, issue.5507, pp.1304-1351, 2001.
URL : https://hal.archives-ouvertes.fr/hal-00465088

B. J. Vilhjalmsson, J. Yang, H. K. Finucane, A. Gusev, S. Lindstrom et al., Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores, Am. J. Hum. Genet, vol.97, issue.4, p.96, 2015.

P. M. Visscher, W. G. Hill, and N. R. Wray, Heritability in the genomics era -Concepts and misconceptions, Nat. Rev. Genet, vol.9, issue.4, p.11, 2008.

P. M. Visscher, N. R. Wray, Q. Zhang, P. Sklar, M. I. Mccarthy et al., 10 Years of GWAS Discovery: Biology, Function, and Translation, Am. J. Hum. Genet, vol.101, issue.1, pp.5-22, 2017.

B. Vogelstein, N. Papadopoulos, V. E. Velculescu, S. Zhou, L. A. Diaz et al., Cancer Genome Landscapes. Science (80-. ), vol.339, issue.6127, p.33, 2013.

M. J. Wainwright, Sharp thresholds for high-dimensional and noisy sparsity recovery using l1-constrained quadratic programming (Lasso), IEEE Trans. Inf. Theory, vol.55, issue.5, p.77, 2009.

J. Wang, J. Zhou, P. Wonka, and J. Ye, Lasso screening rules via dual polytope projection, Adv. Neural Inf. Process. Syst, p.77, 2013.

J. Wang, J. J. Gamsby, S. L. Highfill, L. B. Mora, G. C. Bloom et al., Deregulated expression of LRBA facilitates cancer cell growth, Oncogene, vol.23, issue.23, p.41, 2004.

K. Wang, H. Gaitsch, H. Poon, N. J. Cox, and A. Rzhetsky, Classification of common human diseases derived from shared genetic and environmental determinants, Nat. Genet, vol.49, issue.9, p.11, 2017.

D. J. Watts and S. H. Strogatz, Collective dynamics of 'small-world' networks, Nature, vol.393, issue.6684, p.53, 1998.

M. N. Weedon, H. Lango, C. M. Lindgren, C. Wallace, D. M. Evans et al., Genome-wide association analysis identifies 20 loci that influence adult height, Nat. Genet, vol.40, issue.5, p.11, 2008.

B. Weigelt and J. Downward, Genomic Determinants of PI3K Pathway Inhibitor Response in Cancer, Front. Oncol, vol.2, issue.9, p.109, 2012.

D. Welter, J. Macarthur, J. Morales, T. Burdett, P. Hall et al., The NHGRI GWAS Catalog, a curated resource of SNP-trait associations, Nucleic Acids Res, vol.45, issue.10, pp.896-901, 2014.

D. H. Wolpert, The Supervised Learning No-Free Lunch Theorems, In Soft Comput. Ind, p.14, 2002.

D. H. Wolpert and W. G. Macready, No free lunch theorems for optimization, IEEE Trans. Evol. Comput, vol.1, issue.1, p.14, 1997.

L. D. Wood, D. W. Parsons, S. Jones, J. Lin, T. Sjöblom et al., The genomic landscapes of human breast and colorectal cancers, Science, vol.318, issue.5853, p.33, 2007.

T. T. Wu, Y. F. Chen, T. Hastie, E. Sobel, and K. Lange, Genome-wide association analysis by lasso penalized logistic regression, Bioinformatics, vol.25, issue.6, p.78, 2009.

Z. Xiang, H. Xu, and P. Ramadge, Learning sparse representations of high dimensional data on large scale dictionaries, Adv. Neural Inf. Process. Syst, p.77, 2011.

Z. J. Xiang and P. J. Ramadge, Fast lasso screening tests based on correlations, IEEE Int. Conf. Acoust. Speech Signal Process, p.77, 2012.

Y. Xie, A. Kapoor, H. Peng, J. Cutz, L. Tao et al., IQGAP2 Displays Tumor Suppression Functions, J. Anal. Oncol, vol.4, issue.2, p.41, 2015.

J. Yang, B. Benyamin, B. P. Mcevoy, S. Gordon, A. K. Henders et al., Common SNPs explain a large proportion of heritability for human height, Nat. Genet, vol.42, issue.7, p.11, 2010.

Y. H. Yang and N. P. Thorne, Normalization for two-color cDNA microarray data, Lecture Notes-Monograph Series, vol.40, p.61, 2003.

P. Yousefi, K. Huen, R. Schall, A. Decker, E. Elboudwarej et al., Considerations for normalization of DNA methylation data by Illumina 450K BeadChip assay in population studies, Epigenetics, vol.8, issue.11, p.61, 2013.

Y. Yuan, E. M. Allen, L. Omberg, N. Wagle, A. Amin-mansour et al., Assessing the clinical utility of cancer genomic and proteomic data across tumor types, Nat. Biotechnol, vol.32, issue.7, p.34, 2014.

T. Yue and H. Wang, Deep Learning for Genomics: A Concise Overview, p.20, 2018.

E. Zeggini, L. J. Scott, R. Saxena, B. F. Voight, and F. S. Collins, Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes, Nat. Genet, vol.40, issue.5, p.11, 2008.

D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf, Learning with local and global consistency, Adv. Neural Inf. Process. Syst, p.36, 2004.

A. Zinovyev, U. Kairov, T. Karpenyuk, and E. Ramanculov, Blind source separation methods for deconvolution of complex signals in cancer biology, Biochem. Biophys. Res. Commun, vol.430, issue.3, p.19, 2013.

O. Zuk, E. Hechter, S. R. Sunyaev, and E. S. Lander, The mystery of missing heritability: Genetic interactions create phantom heritability, Proc. Natl. Acad. Sci, vol.109, issue.4, p.12, 2012.