Semi-supervised network inference using simulated gene expression dynamics.

ABSTRACT:

Motivation

Inferring the structure of gene regulatory networks from high-throughput datasets remains an important and unsolved problem. Current methods are hampered by problems such as noise, low sample size, and incomplete characterizations of regulatory dynamics, leading to networks with missing and anomalous links. Integration of prior network information (e.g. from pathway databases) has the potential to improve reconstructions.

Results

We developed a semi-supervised network reconstruction algorithm that enables the synthesis of information from partially known networks with time course gene expression data. We adapted partial least square-variable importance in projection (VIP) for time course data and used reference networks to simulate expression data from which null

SUBMITTER: Nguyen P 

PROVIDER: S-EPMC6455938 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature