Fast and accurate gene regulatory network inference by normalized least squares regression.

ABSTRACT:

Motivation

Inferring an accurate gene regulatory network (GRN) has long been a key goal in the field of systems biology. To do this, it is important to find a suitable balance between the maximum number of true positive and the minimum number of false-positive interactions. Another key feature is that the inference method can handle the large size of modern experimental data, meaning the method needs to be both fast and accurate. The Least Squares Cut-Off (LSCO) method can fulfill both these criteria, however as it is based on least squares it is vulnerable to known issues of amplifying extreme values, small or large. In GRN this manifests itself with genes that are erroneously hyper-connected to a large fraction of all genes due to extremely low value fold changes.

Results

SUBMITTER: Hillerton T 

PROVIDER: S-EPMC9004640 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature