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Biostatistics (Oxford, England), ISSN 1465-4644, 09/2018
We consider high-dimensional regression over subgroups of observations. Our work is motivated by biomedical problems, where subsets of samples, representing... 
Journal Article
Journal of the Royal Statistical Society: Series B (Statistical Methodology), ISSN 1369-7412, 01/2017, Volume 79, Issue 1, pp. 225 - 246
Journal Article
Nature Communications, ISSN 2041-1723, 05/2014, Volume 5, Issue 1, pp. 3887 - 3887
Protein levels and function are poorly predicted by genomic and transcriptomic analysis of patient tumours. Therefore, direct study of the functional proteome... 
BREAST-CANCER | PATHWAY ACTIVATION | MESSENGER-RNA | MULTIDISCIPLINARY SCIENCES | PROTEIN EXPRESSION | COPY NUMBER | SQUAMOUS-CELL CARCINOMA | GENE AMPLIFICATION | E-CADHERIN | HER2 | MICROARRAY ANALYSIS | Index Medicus
Journal Article
Journal of Machine Learning Research, ISSN 1532-4435, 2014, Volume 33, pp. 687 - 695
Journal Article
by Hill, Steven M and Heiser, Laura M and Cokelaer, Thomas and Linger, Michael and Nesser, Nicole K and Carlin, Daniel E and Zhang, Yang and Sokolov, Artem and Paull, Evan O and Wong, Chris K and Graim, Kiley and Bivol, Adrian and Wang, Haizhou and Zhu, Fan and Afsari, Bahman and Danilova, Ludmila V and Favorov, Alexander V and Lee, Wai Shing and Taylor, Dane and Hu, Chenyue W and Long, Byron L and Noren, David P and Bisberg, Alexander J and Mills, Gordon B and Gray, Joe W and Kellen, Michael and Norman, Thea and Friend, Stephen and Qutub, Amina A and Fertig, Elana J and Guan, Yuanfang and Song, Mingzhou and Stuart, Joshua M and Spellman, Paul T and Koeppl, Heinz and Stolovitzky, Gustavo and Saez-Rodriguez, Julio and Mukherjee, Sach and Al-Ouran, Rami and Anton, Bernat and Arodz, Tomasz and Sichani, Omid Askari and Bagheri, Neda and Berlow, Noah and Bohler, Anwesha and Bonet, Jaume and Bonneau, Richard and Budak, Gungor and Bunescu, Razvan and Caglar, Mehmet and Cai, Binghuang and Cai, Chunhui and Carlon, Azzurra and Chen, Lujia and Ciaccio, Mark F and Cooper, Gregory and Coort, Susan and Creighton, Chad J and Daneshmand, Seyed-Mohammad-Hadi and De La Fuente, Alberto and Di Camillo, Barbara and Dutta-Moscato, Joyeeta and Emmett, Kevin and Evelo, Chris and Fassia, Mohammad-Kasim H and Finotello, Francesca and Finkle, Justin D and Gao, Xi and Garcia-Garcia, Javier and Ghosh, Samik and Giaretta, Alberto and Großeholz, Ruth and Guinney, Justin and Hafemeister, Christoph and Hahn, Oliver and Haider, Saad and Hase, Takeshi and Hodgson, Jay and Hoff, Bruce and Hsu, Chih Hao and Hu, Ying and Huang, Xun and Jalili, Mahdi and Jiang, Xia and Kacprowski, Tim and Kaderali, Lars and Kang, Mingon and Kannan, Venkateshan and Kikuchi, Kaito and Kim, Dong-Chul and Kitano, Hiroaki and Knapp, Bettina and Komatsoulis, George and Krämer, Andreas and Kursa, Miron Bartosz and Kutmon, Martina and Li, Yichao and Liang, Xiaoyu and Liu, Zhaoqi and Liu, Yu and ... and HPN-DREAM Consortium and The HPN-DREAM Consortium
Nature Methods, ISSN 1548-7091, 03/2016, Volume 13, Issue 4, pp. 310 - 322
Journal Article
Biostatistics, ISSN 1465-4644, 01/2015, Volume 16, Issue 1, pp. 47 - 59
Journal Article
International Journal of Quantum Chemistry, ISSN 0020-7608, 2005, Volume 101, Issue 2, pp. 119 - 126
A method for construction of the characteristic polynomial (CP) coefficients of the three classes of reciprocal graphs, viz., L n + n ( p ), C n + n ( p ), and... 
polynomial | reciprocal graphs | pendant vertices | Polynomial | Pendant vertices | Reciprocal graphs | ORBITAL FUNCTIONAL-GROUPS | PI-ELECTRON ENERGY | COMPLEXES | PHYSICS, ATOMIC, MOLECULAR & CHEMICAL | EQUATIONS | CHEMISTRY, PHYSICAL | EIGENVALUES | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | TREES | CHEMICAL GRAPHS | SACHS THEOREM | MOLECULAR-ORBITALS | SYSTEMS
Journal Article
The Annals of Applied Statistics, ISSN 1932-6157, 12/2013, Volume 7, Issue 4, pp. 2157 - 2179
Journal Article
Journal of Machine Learning Research, ISSN 1532-4435, 04/2016, Volume 17, Issue 30, pp. 1 - 39
We propose a Gibbs sampler for structure learning in directed acyclic graph (DAG) models. The standard Markov chain Monte Carlo algorithms used for learning... 
Structure learning | Bayesian networks | Gibbs sampling | DAGs | Markov chain Monte Carlo | Variable selection | ALGORITHM | variable selection | STRUCTURE DISCOVERY | structure learning | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | GRAPHS
Journal Article
Journal of Computational and Graphical Statistics, ISSN 1061-8600, 07/2019, pp. 1 - 22
Applications of high-dimensional regression often involve multiple sources or types of covariates. We propose methodology for this setting, emphasizing the... 
Journal Article
The Annals of Applied Statistics, ISSN 1932-6157, 9/2012, Volume 6, Issue 3, pp. 1209 - 1235
Journal Article
2009, Volume 3, 6
Reference
Scientific Reports, ISSN 2045-2322, 01/2014, Volume 4, Issue 1, pp. 3692 - 3692
Journal Article
Journal of Machine Learning Research, ISSN 1532-4435, 04/2016, Volume 17
This paper considers inference of causal structure in a class of graphical models called conditional DAGs. These are directed acyclic graph (DAG) models with... 
Causal inference | Graphical models | Instrumental variables | Data integration | Directed acyclic graphs | Causal Inference | Directed Acyclic Graphs | FRAMEWORK | NETWORK STRUCTURE | Instrumental Variables | INFERENCE | Graphical Models | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Data Integration
Journal Article