-
1
-
-
0033691754
-
Using Bayesian networks to analyze expression data
-
ACM Press
-
Nir Friedman, Michal Linial, Iftach Nachman, and Dana Pe'er. Using Bayesian networks to analyze expression data. In RECOMB 4, pages 127-135. ACM Press, 2000.
-
(2000)
RECOMB
, vol.4
, pp. 127-135
-
-
Friedman, N.1
Linial, M.2
Nachman, I.3
Pe'er, D.4
-
2
-
-
33751407959
-
Computational inference of neural information flow networks
-
DOI 10.1371/journal.pcbi.0020161
-
V. Anne Smith, Jing Yu, Tom V. Smulders, Alexander J. Hartemink, and Erich D. Jarvis. Computational inference of neural information flow networks. PLoS Computational Biology, 2(11):1436-1449, 2006. (Pubitemid 44819929)
-
(2006)
PLoS Computational Biology
, vol.2
, Issue.11
, pp. 1436-1449
-
-
Smith, V.A.1
Yu, J.2
Smulders, T.V.3
Hartemink, A.J.4
Jarvis, E.D.5
-
4
-
-
84858782531
-
Recovering temporally rewiring networks: A model-based approach
-
Fan Guo, Steve Hanneke, Wenjie Fu, and Eric P. Xing. Recovering temporally rewiring networks: A model-based approach. In ICML 24, 2007.
-
(2007)
ICML
, vol.24
-
-
Guo, F.1
Hanneke, S.2
Fu, W.3
Xing, E.P.4
-
5
-
-
20744459144
-
Structural learning with time-varying components: Tracking the cross-section of financial time series
-
DOI 10.1111/j.1467-9868.2005.00504.x
-
Makram Talih and Nicolas Hengartner. Structural learning with time-varying components: Tracking the cross-section of financial time series. Journal of the Royal Statistical Society B, 67(3):321-341, 2005. (Pubitemid 40855287)
-
(2005)
Journal of the Royal Statistical Society. Series B: Statistical Methodology
, vol.67
, Issue.3
, pp. 321-341
-
-
Talih, M.1
Hengartner, N.2
-
6
-
-
77956512686
-
Modeling changing dependency structure in multivariate time series
-
Xiang Xuan and Kevin Murphy. Modeling changing dependency structure in multivariate time series. In ICML 24, 2007.
-
(2007)
ICML
, vol.24
-
-
Xuan, X.1
Murphy, K.2
-
7
-
-
34249761849
-
Learning Bayesian networks: The combination of knowledge and statistical data
-
David Heckerman, Dan Geiger, and David Maxwell Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3):197-243, 1995.
-
(1995)
Machine Learning
, vol.20
, Issue.3
, pp. 197-243
-
-
Heckerman, D.1
Geiger, D.2
Chickering, D.M.3
-
8
-
-
84993704988
-
MCMC model determination for discrete graphical models
-
Claudia Tarantola. MCMC model determination for discrete graphical models. Statistical Modelling, 4(1):39-61, 2004.
-
(2004)
Statistical Modelling
, vol.4
, Issue.1
, pp. 39-61
-
-
Tarantola, C.1
-
9
-
-
0032273105
-
Learning probabilistic networks
-
P Krause. Learning probabilistic networks. The Knowledge Engineering Review, 13(4):321-351, 1998. (Pubitemid 128659264)
-
(1998)
Knowledge Engineering Review
, vol.13
, Issue.4
, pp. 321-351
-
-
Krause, P.J.1
-
10
-
-
79551504327
-
Learning Bayesian network structure from sparse data sets
-
University of California at Berkeley
-
Kevin Murphy. Learning Bayesian network structure from sparse data sets. U.C. Berkeley Technical Report, Computer Science Department 990, University of California at Berkeley, 2001.
-
(2001)
U.C. Berkeley Technical Report, Computer Science Department 990
-
-
Murphy, K.1
-
11
-
-
77956889087
-
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
-
Peter J. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4):711-732, 1995.
-
(1995)
Biometrika
, vol.82
, Issue.4
, pp. 711-732
-
-
Green, P.J.1
-
12
-
-
0037183901
-
Gene expression during the life cycle of Drosophila melanogaster
-
M Arbeitman, E Furlong, F Imam, E Johnson, B Null, B Baker, M Krasnow, M Scott, R Davis, and K White. Gene expression during the life cycle of Drosophila melanogaster. Science, 5590(297):2270-2275, 2002.
-
(2002)
Science
, vol.5590
, Issue.297
, pp. 2270-2275
-
-
Arbeitman, M.1
Furlong, E.2
Imam, F.3
Johnson, E.4
Null, B.5
Baker, B.6
Krasnow, M.7
Scott, M.8
Davis, R.9
White, K.10
-
13
-
-
33748654580
-
Inferring gene regulatory networks from time series data using the minimum description length principle
-
DOI 10.1093/bioinformatics/btl364
-
Wentao Zhao, Erchin Serpedin, and Edward R. Dougherty. Inferring gene regulatory networks from time series data using the minimum description length principle. Bioinformatics, 22(17):2129-2135, 2006. (Pubitemid 44390904)
-
(2006)
Bioinformatics
, vol.22
, Issue.17
, pp. 2129-2135
-
-
Zhao, W.1
Serpedin, E.2
Dougherty, E.R.3
-
14
-
-
33646858607
-
A temporal map of transcription factor activity: Mef2 directly regulates target genes at all stages of muscle development
-
DOI 10.1016/j.devcel.2006.04.009, PII S1534580706001705
-
T Sandmann, L Jensen, J Jakobsen, MKarzynski, MEichenlaub, P Bork, and E Furlong. A temporal map of transcription factor activity: mef2 directly regulates target genes at all stages of muscle development. Developmental Cell, 10(6):797-807, 2006. (Pubitemid 43779116)
-
(2006)
Developmental Cell
, vol.10
, Issue.6
, pp. 797-807
-
-
Sandmann, T.1
Jensen, L.J.2
Jakobsen, J.S.3
Karzynski, M.M.4
Eichenlaub, M.P.5
Bork, P.6
Furlong, E.E.M.7
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