Adaptive multi-task Lasso: With application to eQTL detection

Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

Volumn , Issue , 2010, Pages

  Lee, Seunghak ^a   Zhu, Jun ^a   Xing, Eric P ^a  

^a Carnegie Mellon University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; BINDING SITES; COMPLEX NETWORKS; GENES; ITERATIVE METHODS; LEARNING SYSTEMS;

COMPLEX DISEASE; COVARIATES; DATA INFORMATIONS; GENETIC LOCUS; GENOMIC LOCATIONS; GENOMICS; MULTI TASKS; NUMBER OF SAMPLES; PRIOR INFORMATION; TRANSCRIPTION FACTOR BINDING SITES;

GENOME;

EID: 85162048536     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. R. Sladek, G. Rocheleau, J. Rung, C. Dina, L. Shen, D. Serre, P. Boutin, D. Vincent, A. Belisle, S. Hadjadj, et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature, 445(7130):881-885, 2007.
2. R. Tibshirani. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1):267-288, 1996.
3. S.I. Lee, A.M. Dudley, D. Drubin, P.A. Silver, N.J. Krogan, D. Pe'er, and D. Koller. Learning a prior on regulatory potential from eQTL data. PLoS Genetics, 5(1):e1000358, 2009.
4. S. Kim and E. P. Xing. Statistical estimation of correlated genome associations to a quantitative trait network. PLoS Genetics, 5(8):e1000587, 2009.
5. G. Obozinski, B. Taskar, and M. Jordan. Multi-task feature selection. In Technical Report, Department of Statistics, University of California, Berkeley, 2006.
6. M. Szafranski, Y. Grandvalet, and P. Morizet-Mahoudeaux. Hierarchical penalization. Advances in Neural Information Processing Systems, 20:1457-1464, 2007.
7. H. Zou. The adaptive Lasso and its oracle properties. Journal of the American Statistical Association, 101(476):1418-1429, 2006.
8. S.I. Lee, V. Chatalbashev, D. Vickrey, and D. Koller. Learning a meta-level prior for feature relevance from multiple related tasks. In Proceedings of the 24th International Conference on Machine Learning, pages 489-496, 2007.
9. T. Park and G. Casella. The bayesian Lasso. Journal of the American Statistical Association, 103(482):681-686, 2008.
10. B.M.Marlin,M. Schmidt, and K. P.Murphy. Group sparse priors for covariance estimation. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, pages 383-392, 2009.
11. E. Gómez, M. A. Gomez-Viilegas, and J. M. Marin. A multivariate generalization of the power exponential family of distributions. Communications in Statistics-Theory and Methods, 27(3):589-600, 1998.
12. H. Lee, A. Battle, R. Raina, and A. Y. Ng. Efficient sparse coding algorithms. Advances in Neural Information Processing Systems, 19:801-808, 2007.
13. 1-ball for learning in high dimensions. In Proceedings of the 25th International Conference on Machine Learning, pages 272-279, 2008.
14. J. Friedman, T. Hastie, and R. Tibshirani. A note on the group Lasso and a sparse group Lasso. arXiv:1001.0736v1 [math.ST], 2010.
15. T. T. Wu and K. Lange. Coordinate descent algorithms for Lasso penalized regression. Ann. Appl. Stat, 2(1):224-244, 2008.
16. R. B. Brem and L. Kruglyak. The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proceedings of the National Academy of Sciences of the United States of America, 102(5):1572-1577, 2005.
17. S. Purcell, B. Neale, K. Todd-Brown, L. Thomas, M. A. R. Ferreira, D. Bender, J. Maller, P. Sklar, P. I. W. De Bakker,M. J. Daly, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics, 81(3):559-575, 2007.
18. G. Storz. An expanding universe of noncoding RNAs. Science, 296(5571):1260-1263, 2002.
19. T. Miyake, J. Reese, C. M. Loch, D. T. Auble, and R. Li. Genome-wide analysis of ARS (autonomously replicating sequence) binding factor 1 (Abf1p)-mediated transcriptional regulation in Saccharomyces cerevisiae. Journal of Biological Chemistry, 279(33):34865-34872, 2004.
20. G. Yvert, R. B. Brem, J. Whittle, J. M. Akey, E. Foss, E. N. Smith, R. Mackelprang, L. Kruglyak, et al. Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nature Genetics, 35(1):57-64, 2003.