Functional networks inference from rule-based machine learning models.

ABSTRACT:

Background

Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods.

Results

We propose a protocol to infer functional networks from machine learning models, called

SUBMITTER: Lazzarini N 

PROVIDER: S-EPMC5011349 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature