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, and 3) the number of parameters to learn is usually larger than the number of available microarray experiments. We present a sparse (L1 regularized) graphical model to address these challenges. Our model incorporates known transcription factors and introduces hidden variables to represent possible unknown transcription and confounding factors. The exp...
UnrestrictedUnderstanding the gene regulatory network has always been one of the important and chall...
(A) Our network inference algorithm takes as input a gene expression matrix, X, and a prior on netwo...
We propose a kernel-based method for inferring regulatory networks from gene expression data that ex...
Understanding the organization and function of transcriptional regulatory networks by analyzing high...
Gene expression is a readily-observed quantification of transcriptional activity and cellular state ...
Motivation: Genetic networks regulate key processes in living cells. Various methods have been sugge...
<div><p>Integrating genetic perturbations with gene expression data not only improves accuracy of re...
Identifying gene regulatory networks (GRNs) which consist of a large number of interacting units has...
Background: Transcriptional gene regulation is one of the most important mechanisms in controlling m...
Motivation: Many modeling frameworks have been applied to infer regulatory networks from gene expres...
Abstract The problem of uncovering transcriptional regulation by transcription factors (TFs) based o...
Inferring the combinatorial regulatory code of transcription factors (TFs) from genome-wide TF bindi...
Abstract Background To understand the molecular mecha...
<div><p>Motivation</p><p>Identifying gene regulatory networks (GRNs) which consist of a large number...
The regulation and responses of genes involve complex systems of relationships between genes, protei...
UnrestrictedUnderstanding the gene regulatory network has always been one of the important and chall...
(A) Our network inference algorithm takes as input a gene expression matrix, X, and a prior on netwo...
We propose a kernel-based method for inferring regulatory networks from gene expression data that ex...
Understanding the organization and function of transcriptional regulatory networks by analyzing high...
Gene expression is a readily-observed quantification of transcriptional activity and cellular state ...
Motivation: Genetic networks regulate key processes in living cells. Various methods have been sugge...
<div><p>Integrating genetic perturbations with gene expression data not only improves accuracy of re...
Identifying gene regulatory networks (GRNs) which consist of a large number of interacting units has...
Background: Transcriptional gene regulation is one of the most important mechanisms in controlling m...
Motivation: Many modeling frameworks have been applied to infer regulatory networks from gene expres...
Abstract The problem of uncovering transcriptional regulation by transcription factors (TFs) based o...
Inferring the combinatorial regulatory code of transcription factors (TFs) from genome-wide TF bindi...
Abstract Background To understand the molecular mecha...
<div><p>Motivation</p><p>Identifying gene regulatory networks (GRNs) which consist of a large number...
The regulation and responses of genes involve complex systems of relationships between genes, protei...
UnrestrictedUnderstanding the gene regulatory network has always been one of the important and chall...
(A) Our network inference algorithm takes as input a gene expression matrix, X, and a prior on netwo...
We propose a kernel-based method for inferring regulatory networks from gene expression data that ex...