论文信息 - Bayesian Methods for Elucidating Genetic Regulatory Networks - 字舞流文

Bayesian Methods for Elucidating Genetic Regulatory Networks

Bayesian network methods are useful for elucidating genetic regulatory networks because they can represent more than pair-wise relationships between variables, are resistant to overfitting, and remain robust in the face of noisy data.

Tommi S. Jaakkola | Richard A. Young | David K. Gifford | Alexander J. Hartemink | Richard A. Young | T. Jaakkola | D. Gifford | R. Young | A. Hartemink

[1] David Maxwell Chickering,et al. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[2] Tommi S. Jaakkola,et al. Combining Location and Expression Data for Principled Discovery of Genetic Regulatory Network Models , 2001, Pacific Symposium on Biocomputing.

[3] Tommi S. Jaakkola,et al. Maximum-likelihood estimation of optimal scaling factors for expression array normalization , 2001, SPIE BiOS.

[4] Nir Friedman,et al. Inferring subnetworks from perturbed expression profiles , 2001, ISMB.

[5] Tommi S. Jaakkola,et al. Fast optimal leaf ordering for hierarchical clustering , 2001, ISMB.

[6] Tommi S. Jaakkola,et al. Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks , 2000, Pacific Symposium on Biocomputing.

[7] Michal Linial,et al. Using Bayesian networks to analyze expression data , 2000, RECOMB '00.

[8] Ash A. Alizadeh,et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[9] Michael I. Jordan,et al. Variational Probabilistic Inference and the QMR-DT Network , 2011, J. Artif. Intell. Res..

[10] M. Johnston. A model fungal gene regulatory mechanism: the GAL genes of Saccharomyces cerevisiae. , 1987, Microbiological reviews.