76 related articles for article (PubMed ID: 20238419)
1. A state space representation of VAR models with sparse learning for dynamic gene networks.
Kojima K; Yamaguchi R; Imoto S; Yamauchi M; Nagasaki M; Yoshida R; Shimamura T; Ueno K; Higuchi T; Gotoh N; Miyano S
Genome Inform; 2010 Jan; 22():56-68. PubMed ID: 20238419
[TBL] [Abstract][Full Text] [Related]
2. Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing.
Kojima K; Imoto S; Yamaguchi R; Fujita A; Yamauchi M; Gotoh N; Miyano S
BMC Genomics; 2012; 13 Suppl 1(Suppl 1):S6. PubMed ID: 22369122
[TBL] [Abstract][Full Text] [Related]
3. Collocation-based sparse estimation for constructing dynamic gene networks.
Shimamura T; Imoto S; Nagasaki M; Yamauchi M; Yamaguchi R; Fujita A; Tamada Y; Gotoh N; Miyano S
Genome Inform; 2010; 24():164-78. PubMed ID: 22081598
[TBL] [Abstract][Full Text] [Related]
4. Weighted lasso in graphical Gaussian modeling for large gene network estimation based on microarray data.
Shimamura T; Imoto S; Yamaguchi R; Miyano S
Genome Inform; 2007; 19():142-53. PubMed ID: 18546512
[TBL] [Abstract][Full Text] [Related]
5. Predicting differences in gene regulatory systems by state space models.
Yamaguchi R; Imoto S; Yamauchi M; Nagasaki M; Yoshida R; Shimamura T; Hatanaka Y; Ueno K; Higuchi T; Gotoh N; Miyano S
Genome Inform; 2008; 21():101-13. PubMed ID: 19425151
[TBL] [Abstract][Full Text] [Related]
6. Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models.
Hirose O; Yoshida R; Imoto S; Yamaguchi R; Higuchi T; Charnock-Jones DS; Print C; Miyano S
Bioinformatics; 2008 Apr; 24(7):932-42. PubMed ID: 18292116
[TBL] [Abstract][Full Text] [Related]
7. Identification of biochemical networks by S-tree based genetic programming.
Cho DY; Cho KH; Zhang BT
Bioinformatics; 2006 Jul; 22(13):1631-40. PubMed ID: 16585066
[TBL] [Abstract][Full Text] [Related]
8. Estimation of nonlinear gene regulatory networks via L1 regularized NVAR from time series gene expression data.
Kojima K; Fujita A; Shimamura T; Imoto S; Miyano S
Genome Inform; 2008; 20():37-51. PubMed ID: 19425121
[TBL] [Abstract][Full Text] [Related]
9. Fast calculation of pairwise mutual information for gene regulatory network reconstruction.
Qiu P; Gentles AJ; Plevritis SK
Comput Methods Programs Biomed; 2009 May; 94(2):177-80. PubMed ID: 19167129
[TBL] [Abstract][Full Text] [Related]
10. Stochastic dynamic modeling of short gene expression time-series data.
Wang Z; Yang F; Ho DW; Swift S; Tucker A; Liu X
IEEE Trans Nanobioscience; 2008 Mar; 7(1):44-55. PubMed ID: 18334455
[TBL] [Abstract][Full Text] [Related]
11. Analysis of bypass signaling in EGFR pathway and profiling of bypass genes for predicting response to anticancer EGFR tyrosine kinase inhibitors.
Zhang J; Jia J; Zhu F; Ma X; Han B; Wei X; Tan C; Jiang Y; Chen Y
Mol Biosyst; 2012 Oct; 8(10):2645-56. PubMed ID: 22833077
[TBL] [Abstract][Full Text] [Related]
12. A new multiple regression approach for the construction of genetic regulatory networks.
Zhang SQ; Ching WK; Tsing NK; Leung HY; Guo D
Artif Intell Med; 2010; 48(2-3):153-60. PubMed ID: 19963359
[TBL] [Abstract][Full Text] [Related]
13. Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method.
Fujita A; Sato JR; Garay-Malpartida HM; Morettin PA; Sogayar MC; Ferreira CE
Bioinformatics; 2007 Jul; 23(13):1623-30. PubMed ID: 17463021
[TBL] [Abstract][Full Text] [Related]
14. Gene selection in cancer classification using sparse logistic regression with Bayesian regularization.
Cawley GC; Talbot NL
Bioinformatics; 2006 Oct; 22(19):2348-55. PubMed ID: 16844704
[TBL] [Abstract][Full Text] [Related]
15. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data.
Zou M; Conzen SD
Bioinformatics; 2005 Jan; 21(1):71-9. PubMed ID: 15308537
[TBL] [Abstract][Full Text] [Related]
16. A Markovian approach to the control of genetic regulatory networks.
Chen PC; Chen JW
Biosystems; 2007; 90(2):535-45. PubMed ID: 17320274
[TBL] [Abstract][Full Text] [Related]
17. Antitumor activity of the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor gefitinib (ZD1839, Iressa) in non-small cell lung cancer cell lines correlates with gene copy number and EGFR mutations but not EGFR protein levels.
Helfrich BA; Raben D; Varella-Garcia M; Gustafson D; Chan DC; Bemis L; Coldren C; Barón A; Zeng C; Franklin WA; Hirsch FR; Gazdar A; Minna J; Bunn PA
Clin Cancer Res; 2006 Dec; 12(23):7117-25. PubMed ID: 17145836
[TBL] [Abstract][Full Text] [Related]
18. Estimating sparse gene regulatory networks using a bayesian linear regression.
Sarder P; Schierding W; Cobb JP; Nehorai A
IEEE Trans Nanobioscience; 2010 Jun; 9(2):121-31. PubMed ID: 20650703
[TBL] [Abstract][Full Text] [Related]
19. Sparse time series chain graphical models for reconstructing genetic networks.
Abegaz F; Wit E
Biostatistics; 2013 Jul; 14(3):586-99. PubMed ID: 23462022
[TBL] [Abstract][Full Text] [Related]
20. Epidermal growth factor receptor mutations are associated with gefitinib sensitivity in non-small cell lung cancer in Japanese.
Uramoto H; Sugio K; Oyama T; Ono K; Sugaya M; Yoshimatsu T; Hanagiri T; Morita M; Yasumoto K
Lung Cancer; 2006 Jan; 51(1):71-7. PubMed ID: 16198442
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
