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Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning
Örebro University, School of Science and Technology. Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden. (MTM Research Centre)ORCID iD: 0000-0003-3107-331X
Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, London, UK.
2021 (English)In: Journal of Cheminformatics, E-ISSN 1758-2946, Vol. 13, no 1, article id 77Article in journal (Refereed) Published
Abstract [en]

Confidence predictors can deliver predictions with the associated confidence required for decision making and can play an important role in drug discovery and toxicity predictions. In this work we investigate a recently introduced version of conformal prediction, synergy conformal prediction, focusing on the predictive performance when applied to bioactivity data. We compare the performance to other variants of conformal predictors for multiple partitioned datasets and demonstrate the utility of synergy conformal predictors for federated learning where data cannot be pooled in one location. Our results show that synergy conformal predictors based on training data randomly sampled with replacement can compete with other conformal setups, while using completely separate training sets often results in worse performance. However, in a federated setup where no method has access to all the data, synergy conformal prediction is shown to give promising results. Based on our study, we conclude that synergy conformal predictors are a valuable addition to the conformal prediction toolbox.

Place, publisher, year, edition, pages
Chemistry Central , 2021. Vol. 13, no 1, article id 77
Keywords [en]
Confidence, Conformal prediction, Federated learning, Machine learning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-94787DOI: 10.1186/s13321-021-00555-7ISI: 000702782800002PubMedID: 34600569Scopus ID: 2-s2.0-85116372742OAI: oai:DiVA.org:oru-94787DiVA, id: diva2:1600053
Funder
Swedish Foundation for Strategic Research , BD15-0008SB16-0046
Note

Funding agency:

Uppsala University - Alzheimer's Research UK (ARUK) 560832

Available from: 2021-10-04 Created: 2021-10-04 Last updated: 2024-01-16Bibliographically approved

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Norinder, Ulf

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