Evaluation of categorical matrix completion algorithms: toward improved active learning for drug discovery.

ABSTRACT:

Motivation

High throughput and high content screening are extensively used to determine the effect of small molecule compounds and other potential therapeutics upon particular targets as part of the early drug development process. However, screening is typically used to find compounds that have a desired effect but not to identify potential undesirable side effects. This is because the size of the search space precludes measuring the potential effect of all compounds on all targets. Active machine learning has been proposed as a solution to this problem.

Results

In this article, we describe an improved imputation method, Impute by Committee, for completion of matrices containing categorical values. We compare this method to existing approaches in the context of modeling the

SUBMITTER: Sun H 

PROVIDER: S-EPMC8545350 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature