Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty.

ABSTRACT: Measurements of protein-ligand interactions have reproducibility limits due to experimental errors. Any model based on such assays will consequentially have such unavoidable errors influencing their performance which should ideally be factored into modelling and output predictions, such as the actual standard deviation of experimental measurements (σ) or the associated comparability of activity values between the aggregated heterogenous activity units (i.e., K_i versus IC₅₀ values) during dataset assimilation. However, experimental errors are usually a neglected aspect of model generation. In order to improve upon the current state-of-the-art, we herein present a novel approach toward predicting protein-ligand interactions using a Probabilistic Random Forest (PRF) clas

SUBMITTER: Mervin LH 

PROVIDER: S-EPMC8375213 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature