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Using Predicted Bioactivity Profiles to Improve Predictive Modeling
Örebro University, School of Science and Technology. Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden; Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. (MTM Research Centre)ORCID iD: 0000-0003-3107-331X
Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden; Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
The Alzheimer's Research UK University College London Drug Discovery Institute, London, U.K..
2020 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 60, no 6, p. 2830-2837Article in journal (Refereed) Published
Abstract [en]

Predictive modeling is a cornerstone in early drug development. Using information for multiple domains or across prediction tasks has the potential to improve the performance of predictive modeling. However, aggregating data often leads to incomplete data matrices that might be limiting for modeling. In line with previous studies, we show that by generating predicted bioactivity profiles, and using these as additional features, prediction accuracy of biological endpoints can be improved. Using conformal prediction, a type of confidence predictor, we present a robust framework for the calculation of these profiles and the evaluation of their impact. We report on the outcomes from several approaches to generate the predicted profiles on 16 datasets in cytotoxicity and bioactivity and show that efficiency is improved the most when including the p-values from conformal prediction as bioactivity profiles.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2020. Vol. 60, no 6, p. 2830-2837
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:oru:diva-81934DOI: 10.1021/acs.jcim.0c00250ISI: 000543717300019PubMedID: 32374618Scopus ID: 2-s2.0-85086792719OAI: oai:DiVA.org:oru-81934DiVA, id: diva2:1430997
Note

Funding Agency:

Alzheimer's Research UK (ARUK) 520909

Available from: 2020-05-18 Created: 2020-05-18 Last updated: 2024-01-16Bibliographically approved

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Norinder, Ulf

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