Learning Oncogenic Pathways from Binary Genomic Instability Data
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Publication:3008875
DOI10.1111/j.1541-0420.2010.01417.xzbMath1216.62180arXiv0908.3882OpenAlexW2084805486WikidataQ33550962 ScholiaQ33550962MaRDI QIDQ3008875
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Publication date: 22 June 2011
Published in: Biometrics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/0908.3882
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A sparse ising model with covariates ⋮ An Additive Graphical Model for Discrete Data ⋮ Estimating networks with jumps ⋮ Empirical comparison study of approximate methods for structure selection in binary graphical models
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Cites Work
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- Sparse inverse covariance estimation with the graphical lasso
- Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer
- A model for a multivariate binary response with covariates based on compatible conditionally specified logistic regressions
- Least angle regression. (With discussion)
- Pathwise coordinate optimization
- High-dimensional graphs and variable selection with the Lasso
- Model selection for Gaussian concentration graphs
- Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks
- Model selection and estimation in the Gaussian graphical model
- Introduction to Graphical Modelling
- Bayesian Graphical Models for Discrete Data
- Partial Correlation Estimation by Joint Sparse Regression Models
- Regularization and Variable Selection Via the Elastic Net
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