The influence of negative training set size on machine learning-based virtual screening.

ABSTRACT:

Background

The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods.

Results

The impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. An increase in the ratio of positive to negative training instances was found to greatly influence most of the investigated evaluating parameters of ML methods in simulated virtual screening experiments. In a majority of cases, substantial increases in precision and MCC were observed in conjunction with some decreases in hit recall. The analysis of dynamics of those variations let us reco

SUBMITTER: Kurczab R 

PROVIDER: S-EPMC4061540 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature