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

Autor:	Kupczyk E; Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany.; Comprehensive Foodomics Platform, Chair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 2, 85354 Freising, Germany., Schorpp K; Institute for Molecular Toxicology and Pharmacology, Cell Signaling and Chemical Biology, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany., Hadian K; Institute for Molecular Toxicology and Pharmacology, Cell Signaling and Chemical Biology, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany., Lin S; Institute for Molecular Toxicology and Pharmacology, Cell Signaling and Chemical Biology, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany., Tziotis D; Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany., Schmitt-Kopplin P; Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany.; Comprehensive Foodomics Platform, Chair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 2, 85354 Freising, Germany., Mueller C; Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany.
Jazyk:	angličtina
Zdroj:	Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2022 Sep 27; Vol. 20, pp. 5453-5465. Date of Electronic Publication: 2022 Sep 27 (Print Publication: 2022).
DOI:	10.1016/j.csbj.2022.09.023
Abstrakt:	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 total of 92 models tested). Followed by a partial least square regression (PLSR)-based classification (44 tested models in total) and simple artificial neural networks (ANNs) with dense layers (72 tested models in total). In addition, a novelty detection (ND) was examined, which is supposed to handle unknown patterns. For the final analysis the models, with and without upstream ND, were tested with two independent data sets. In our analysis, a stacking model, an ensamble model of class ML algorithms, performed best to predict new and unknown data. ND improved the predictions of the models and was useful to handle unknown patterns. Importantly, the classifier presented here can be easily rebuilt and be adapted to the data and demands of other groups. The hit detector (ND + stacking model) is universal and suitable for a broader application to support the search for new drug candidates. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2022 The Author(s).)
Databáze:	MEDLINE
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