Unleashing high content screening in hit detection - Benchmarking AI workflows including novelty detection.

ABSTRACT: Complex mixtures containing natural products are still an interesting source of novel drug candidates. High content screening (HCS) is a popular tool to screen for such. In particular, multiplexed HCS assays promise comprehensive bioactivity profiles, but generate also high amounts of data. Yet, only some machine learning (ML) applications for data analysis are available and these usually require a profound knowledge of the underlying cell biology. Unfortunately, there are no applications that simply predict if samples are biologically active or not (any kind of bioactivity). Within this work, we benchmark ML algorithms for binary classification, starting with classical ML models, which are the standard classifiers of the scikit-learn library or ensemble models of these classifiers (a tota

SUBMITTER: Kupczyk E 

PROVIDER: S-EPMC9530837 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature