Using modified lasso regression to learn large undirected graphs in a probabilistic framework

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2005, Pages 801-806

  Li, Fan ^a   Yang, Yiming ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Bayesian networks; Data mining; Machine learning

Indexed keywords

BAYESIAN NETWORKS; GRAPH STRUCTURE; GRAPHICAL GAUSSIAN MODEL (GGM);

COMPUTER GRAPHICS; DATA MINING; GRAPH THEORY; MATRIX ALGEBRA; PROTEINS; REGRESSION ANALYSIS;

LEARNING SYSTEMS;

EID: 29344460888     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Ashburner, M.; Ball, C.; Blake, J.; Botstein, D.; and Butler, H. 2000. Gene ontology: tool for the unifi cation of biology. Nature Genetics 25:25-29.
2. Bar-Joseph, Z.; Gerber, G.; Lee, T.; and Rinaldi, N. 2003. Computational discovery of gene module and regulatory networks. Nature Biotechnology 21(11):1337-42.
3. Bernard, A., and Hartemink, A. 2005. Informative structure priors: Joint learning of dynamic regulatory networks from multiple types of data. In Pacific Symposium on Biocomputing.
4. Dobra, A.; Hans, C.; Jones, B.; Nevins, J.; Yao, G.; and West, M. 2004. Sparse graphical models for exploring gene expression data. J. Mult. Analysis 90:196-212.
5. Figueiredo, M., and Jain, A. 2001. Bayesian learning of sparse classifi ers. In CVPR, 35-41.
6. Friedman, N.; Linial, M.; Nachman, I.; and Pe'er, D. 1996. Using bayesian network to analyze expression data. Jounnal of Computational Biology 7(2002):175-186.
7. Geiger, D., and D.Heckerman. 2002. Parameter priors for directed acyclic graphical models and the characterization of several probability distributions. Annals of Statistics 5:1412-1440.
8. Gustafsson, M.; Hornquist, M.; and Lombardi, A. 2003. Large-scale reverse engineering by the lasso. In ICSB 2003 Poster.
9. Hartemink, A.; Gifford, D.; Jaakkola, T.; and Young, R. 2001. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. In Pacific Symposium on Biocomputing.
10. Lauritzen, S. 1996. Graphical Models.
11. Lee, T.; Rinaldi, N.; Robert, F.; and Odom, D. 2002. Transcriptional regulatory networks in saccharomyces cerevisiae. Science 298:799-804.
12. Meinshausen, N., and Buhlmann, P. 2004. Consistent neighbourhood selection for sparse high-dimensional graphs with lasso. Technical Report Seminar for statistic, ETH 123, Institute for the Study of Learning and Expertise.
13. Ng, A. 2003. Feature selection, 11 vs 12 regularization, and rotational invariance. In ICML.
14. Schafer, J., and Strimmer, K. 2004. An empirical bayes approach to inferring large-scale gene association networks. Bioinformatics in press.
15. Segal, E.; Yelensky, R.; and Koller, D. 2003. Genome-wide discovery of transcriptional modules from dna sequence and gene expression. Bioinformatics 19:273-82.
16. Simon, P.; Kevin, L.; and James, T. 2003. Grafting: Fast,incremental feature selection by gradient descent in function space. JMLR 1333-1356.
17. Spellman, P.; Sherlock, G.; Zhang, M.; and Iyer, V. 1998. Compehensive identifi cation of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. molecular. Biology of the Cell 9:3273-3297.
18. Tibshirani, R. 1996. Optimal reinsertion:regression shrink-age and selection via the lasso. J.R.Statist Soc. B(1996), 58,No.1:267-288.
19. Wille, A.; Zimmermann, P.; Vranov, E.; and Frholz, A. 2004. Sparse graphical gaussian modeling of the iso-prenoid gene network in arabidopsis thaliana. Genome Biology 5:92.