Learning Transcriptional Regulatory Relationships Using Sparse Graphical Models
نویسندگان
چکیده
منابع مشابه
Learning Transcriptional Regulatory Relationships Using Sparse Graphical Models
Understanding the organization and function of transcriptional regulatory networks by analyzing high-throughput gene expression profiles is a key problem in computational biology. The challenges in this work are 1) the lack of complete knowledge of the regulatory relationship between the regulators and the associated genes, 2) the potential for spurious associations due to confounding factors, ...
متن کاملOn Learning Discrete Graphical Models using Group-Sparse Regularization
We study the problem of learning the graph structure associated with a general discrete graphical models (each variable can take any of m > 1 values, the clique factors have maximum size c ≥ 2) from samples, under high-dimensional scaling where the number of variables p could be larger than the number of samples n. We provide a quantitative consistency analysis of a procedure based on node-wise...
متن کاملLearning to Discover Sparse Graphical Models
We consider structure discovery of undirected graphical models from observational data. Inferring likely structures from few examples is a complex task often requiring the formulation of priors and sophisticated inference procedures. In the setting of Gaussian Graphical Models (GGMs) a popular estimator is a maximum likelihood objective with a penalization on the precision matrix. Adapting this...
متن کاملBayesian Learning of Sparse Gaussian Graphical Models
Sparse inverse covariance matrix modeling is an important tool for learning relationships among different variables in a Gaussian graph. Most existing algorithms are based on `1 regularization, with the regularization parameters tuned via cross-validation. In this paper, a Bayesian formulation of the problem is proposed, where the regularization parameters are inferred adaptively and cross-vali...
متن کاملSparse Nonparametric Graphical Models
We present some nonparametric methods for graphical modeling. In the discrete case, where the data are binary or drawn from a finite alphabet, Markov random fields are already essentially nonparametric, since the cliques can take only a finite number of values. Continuous data are different. The Gaussian graphical model is the standard parametric model for continuous data, but it makes distribu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLoS ONE
سال: 2012
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0035762