Low-rank regularization for learning gene expression programs

PLoS ONE

Volumn 8, Issue 12, 2013, Pages

  Ye, Guibo ^a,b   Tang, Mengfan ^a   Cai, Jian Feng ^c   Nie, Qing ^b   Xie, Xiaohui ^a,b  

^a Irvine   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF IOWA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; GENE CONTROL; GENE EXPRESSION REGULATION; GENE EXPRESSION SYSTEM; GENE INTERACTION; GENE NUMBER; GENE TARGETING; GENETIC ALGORITHM; GENETIC DATABASE; HUMAN; LEARNING; MATHEMATICAL COMPUTING; MULTIPLE LINEAR REGRESSION ANALYSIS; NONHUMAN; REGULATOR GENE; YEAST;

ALGORITHMS; GENE EXPRESSION; GENE EXPRESSION REGULATION; HUMANS; MODELS, GENETIC; SOFTWARE;

EID: 84893053905     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0082146     Document Type: Article

References (33)

1. Bansal M, Belcastro V, Ambesi-Impiombato A, Di Bernardo D (2007) How to infer gene networks from expression profiles. Molecular systems biology 3.
2. Lamb J, Crawford E, Peck D, Modell J, Blat I, et al. (2006) The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313: 1929.
3. Friedman N (2004) Inferring cellular networks using probabilistic graphical models. Science 303: 799-805.
4. Friedman J, Hastie T, Tibshirani R (2008) Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9: 432-441.
5. Grzegorczyk M, Husmeier D (2011) Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes. Bioinformatics 27: 693-699.
6. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, et al. (2007) Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS biology 5: e8.
7. Margolin AA, Wang K, Lim WK, Kustagi M, Nemenman I, et al. (2006) Reverse engineering cellular networks. Nature Protocols 1: 662-671.
8. Gustafsson M, Hornquist M, Lombardi A (2005) Constructing and analyzing a large-scale geneto-gene regulatory network lasso-constrained inference and biological validation. Computational Biology and Bioinformatics, IEEE/ACM Transactions on 2: 254-261.
9. Gardner T, Di Bernardo D, Lorenz D, Collins J (2003) Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301: 102.
10. De Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. Journal of computational biology 9: 67-103.
11. Christley S, Nie Q, Xie X (2009) Incorporating existing network information into gene network inference. PloS one 4: e6799.
12. Lee S, Dudley A, Drubin D, Silver P, Krogan N, et al. (2009) Learning a prior on regulatory potential from eqtl data. PLoS genetics 5: e1000358.
13. Ye GB, Wang Y, Chen Y, Xie X (2011) Efficient latent variable graphical model selection via split bregman method. arXiv preprint arXiv:11103076.
14. Tibshirani R (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B (Methodological) : 267-288.
15. Segal E, Shapira M, Regev A, Pe'er D, Botstein D, et al. (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature genetics 34: 166-176.
16. Chiquet J, Smith A, Grasseau G, Matias C, Ambroise C (2009) Simone: Statistical inference for modular networks. Bioinformatics 25: 417-418.
17. Cai J, Candès E, Shen Z (2010) A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization 20: 1956-1982.
18. Chen J, Liu J, Ye J (2012) Learning incoherent sparse and low-rank patterns from multiple tasks. ACM Transactions on Knowledge Discovery from Data (TKDD) 5: 22.
19. Candès E, Li X, Ma Y, Wright J (2011) Robust principal component analysis? Journal of the Association for Computing Machinery 58: 1-37.
20. Ying Y, Huang K, Campbell C (2009) Sparse metric learning via smooth optimization. Advances in Neural Information Processing Systems 22.
21. Hoerl A, Kennard R (1970) Ridge regression: Biased estimation for nonorthogonal problems. Technometrics : 55-67.
22. Candès E, Recht B (2009) Exact matrix completion via convex optimization. Foundations of Computational Mathematics 9: 717-772.
23. Donoho D (2006) Compressed sensing. IEEE Transactions on Information Theory 52: 1289-1306.
24. Cristianini N, Shawe-Taylor J (2004) An introduction to support Vector Machines: and other kernel-based learning methods. Cambridge university press.
25. Schölkopf B, Smola A (2002) Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT press.
26. Vapnik VN (1998) Statistical learning theory. Adaptive and Learning Systems for Signal Processing, Communications, and Control. New York: John Wiley & Sons Inc., xxvi+736 pp. A Wiley-Interscience Publication.
27. Aronszajn N (1950) Theory of reproducing kernels. Trans Amer Math Soc 68: 337-404.
28. Nesterov Y (2005) Smooth minimization of non-smooth functions. Mathematical Programming 103: 127-152.
29. Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2: 183-202.
30. Nesterov Y (2007) Gradient methods for minimizing composite objective function. Center for Operations Research and Econometrics (CORE), Catholic University of Louvain, Tech Rep 76.
31. Gasch A, Spellman P, Kao C, Carmel-Harel O, Eisen M, et al. (2000) Genomic expression programs in the response of yeast cells to environmental changes. Science 11: 4241.
32. Thomas Hofmann BS, Smola AJ (2008) Kernel method in machine learning. The Annals of Statistics 36: 1171.
33. Novershtern N, Subramanian A, Lawton L, Mak R, Haining W, et al. (2011) Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144: 296-309.