Network inference with ensembles of bi-clustering trees.

ABSTRACT:

Background

Network inference is crucial for biomedicine and systems biology. Biological entities and their associations are often modeled as interaction networks. Examples include drug protein interaction or gene regulatory networks. Studying and elucidating such networks can lead to the comprehension of complex biological processes. However, usually we have only partial knowledge of those networks and the experimental identification of all the existing associations between biological entities is very time consuming and particularly expensive. Many computational approaches have been proposed over the years for network inference, nonetheless, efficiency and accuracy are still persisting open problems. Here, we propose bi-clustering tree ensembles as a new machine learning method for

SUBMITTER: Pliakos K 

PROVIDER: S-EPMC6819564 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature