Gene network inference by fusing data from diverse distributions.

ABSTRACT:

Motivation

Markov networks are undirected graphical models that are widely used to infer relations between genes from experimental data. Their state-of-the-art inference procedures assume the data arise from a Gaussian distribution. High-throughput omics data, such as that from next generation sequencing, often violates this assumption. Furthermore, when collected data arise from multiple related but otherwise nonidentical distributions, their underlying networks are likely to have common features. New principled statistical approaches are needed that can deal with different data distributions and jointly consider collections of datasets.

Results

We present FuseNet, a Markov network formulation that infers networks from a collection of nonidentically distributed datasets. Ou

SUBMITTER: Zitnik M 

PROVIDER: S-EPMC4542780 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature