Inferring gene regression networks with model trees

Inferring reticulate evolution networks from consensus gene trees

Motivation: A major unresolved problem in phylogeny is to reconstruct the evolutionary history of a set of species when it cannot be modeled by a simple tree. When a reticulation event, such as hybrid speciation or horizontal transfer, occurs, separate regions of a species’ genetic data may evolve along different trees. Our approach uses the gene trees for each region to solve the inverse probl...

Inferring Gene Transcription Networks: The Davidson Model

Inferring gene regulation networks

A current challenge in system biology is to infer the regulation network of a family of p genes from a n-sample of microarrays, with n (much) smaller than p. Gaussian graphical models are simple models to describe these regulation networks. We propose a procedure that performs Gaussian graph estimation by model selection. We introduce a collection of candidate graphs and then select one of them...

Multi-objective Model Optimization for Inferring Gene Regulatory Networks

With the invention of microarray technology, researchers are able to measure the expression levels of ten thousands of genes in parallel at various time points of a biological process. The investigation of gene regulatory networks has become one of the major topics in Systems Biology. In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data....

Inferring cellular regulatory networks with Bayesian model averaging for linear regression (BMALR).

Bayesian network and linear regression methods have been widely applied to reconstruct cellular regulatory networks. In this work, we propose a Bayesian model averaging for linear regression (BMALR) method to infer molecular interactions in biological systems. This method uses a new closed form solution to compute the posterior probabilities of the edges from regulators to the target gene withi...