Grouped graphical Granger modeling for gene expression regulatory networks discovery
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Naoki Abe | Yan Liu | Saharon Rosset | Aurelie C. Lozano | S. Rosset | N. Abe | Yan Liu | A. Lozano | Saharon Rosset
[1] H. Zou. The Adaptive Lasso and Its Oracle Properties , 2006 .
[2] N. Meinshausen,et al. High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.
[3] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[4] G. Toffolo,et al. CNET: an algorithm for Reverse Engineering of Causal Gene Networks , 2008 .
[5] D. Pe’er,et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.
[6] R. Tibshirani,et al. On the “degrees of freedom” of the lasso , 2007, 0712.0881.
[7] S. Rafii,et al. Splitting vessels: Keeping lymph apart from blood , 2003, Nature Medicine.
[8] Xiaojiang Xu,et al. Learning module networks from genome‐wide location and expression data , 2004, FEBS letters.
[9] C. Granger. Testing for causality: a personal viewpoint , 1980 .
[10] B. Silverman,et al. Nonparametric Regression and Generalized Linear Models: A roughness penalty approach , 1993 .
[11] E. Brambilla,et al. E2F-1, Skp2 and cyclin E oncoproteins are upregulated and directly correlated in high-grade neuroendocrine lung tumors , 2007, Oncogene.
[12] Yan Liu,et al. Temporal causal modeling with graphical granger methods , 2007, KDD '07.
[13] Shun-Wu Fan,et al. [Growth inhibition of MG-63 cells by cyclin A2 gene-specific small interfering RNA]. , 2007, Zhonghua yi xue za zhi.
[14] C. Ball,et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. , 2002, Molecular biology of the cell.
[15] R. Yoshida,et al. Finding module-based gene networks with state-space models - Mining high-dimensional and short time-course gene expression data , 2007, IEEE Signal Processing Magazine.
[16] J Carpenter,et al. Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. , 2000, Statistics in medicine.
[17] B. Silverman,et al. Nonparametric Regression and Generalized Linear Models: A roughness penalty approach , 1993 .
[18] N. Meinshausen,et al. LASSO-TYPE RECOVERY OF SPARSE REPRESENTATIONS FOR HIGH-DIMENSIONAL DATA , 2008, 0806.0145.
[19] P. Jackson,et al. Cyclin E Uses Cdc6 as a Chromatin-Associated Receptor Required for DNA Replication , 2001, The Journal of cell biology.
[20] S. Pandey,et al. What Are Degrees of Freedom , 2008 .
[21] Bernard Ducommun,et al. Moderate variations in CDC25B protein levels modulate the response to DNA damaging agents , 2008, Cell cycle.
[22] Peter Green,et al. Highly Structured Stochastic Systems , 2003 .
[23] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[24] Snigdhansu Chatterjee,et al. Causality and pathway search in microarray time series experiment , 2007, Bioinform..
[25] Korbinian Strimmer,et al. Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process , 2007, BMC Bioinformatics.
[26] João Ricardo Sato,et al. Modeling gene expression regulatory networks with the sparse vector autoregressive model , 2007, BMC Systems Biology.
[27] Anthony C. Davison,et al. Bootstrap Methods and Their Application , 1998 .
[28] D. Ray,et al. CDC25A Levels Determine the Balance of Proliferation and Checkpoint Response , 2007, Cell cycle.
[29] David Page,et al. Modelling regulatory pathways in E. coli from time series expression profiles , 2002, ISMB.
[30] P. Zhao,et al. The composite absolute penalties family for grouped and hierarchical variable selection , 2009, 0909.0411.
[31] W. Enders. Applied Econometric Time Series , 1994 .
[32] Li Li,et al. Discovery of time-delayed gene regulatory networks based on temporal gene expression profiling , 2006, BMC Bioinformatics.
[33] Beryl Rawson,et al. Degrees of Freedom , 2010 .