OscoNet: inferring oscillatory gene networks

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...

Inferring gene regulatory networks by ANOVA

MOTIVATION To improve the understanding of molecular regulation events, various approaches have been developed for deducing gene regulatory networks from mRNA expression data. RESULTS We present a new score for network inference, η(2), that is derived from an analysis of variance. Candidate transcription factor:target gene (TF:TG) relationships are assumed more likely if the expression of TF ...

Inferring Phylogenetic Networks from Gene Order Data

Existing algorithms allow us to infer phylogenetic networks from sequences (DNA, protein or binary), sets of trees, and distance matrices, but there are no methods to build them using the gene order data as an input. Here we describe several methods to build split networks from the gene order data, perform simulation studies, and use our methods for analyzing and interpreting different real gen...

Inferring gene networks from discrete expression data.

The modeling of gene networks from transcriptional expression data is an important tool in biomedical research to reveal signaling pathways and to identify treatment targets. Current gene network modeling is primarily based on the use of Gaussian graphical models applied to continuous data, which give a closed-form marginal likelihood. In this paper, we extend network modeling to discrete data,...

Inferring Gene Transcription Networks: The Davidson Model