Balancing Predictive Relevance of Ligand Biochemical Activities

0565234 - ÚGN 2023 RIV CH eng C - Konferenční příspěvek (zahraniční konf.)
Pecha, Marek
Balancing Predictive Relevance of Ligand Biochemical Activities.
Uncertainty and impresision in decision making and decision support: new advances, challenges, and perspectives. Vol. 338. Cham: Springer International Publishing AG, 2022 - (Atanassov, K.; Atanassova, V.; Kacprzyk, J.; Kaluszko, A.; Krawczak, M.; Owsinski, J.; Sotirov, S.; Sotirova, E.; Szmidt, E.; Zadrozny, S.), s. 338-348. ISBN 978-303095928-9. ISSN 2367-3370. E-ISSN 2367-3389.
[National Conference on Operationaland Systems Research, BOS-2020 /16./ and 19th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, IWIFSGN-2020. Warsaw (PL), 10.12.2020-15.12.2020]
Institucionální podpora: RVO:68145535
Klíčová slova: balancing predictive relevance models * uncalibrated models * Platt’s scaling
Obor OECD: Applied mathematics
https://link.springer.com/chapter/10.1007/978-3-030-95929-6_26

In this paper, we present a technique for balancing predictive relevance models related to supervised modelling ligand biochemical activities to biological targets. We train uncalibrated models employing conventional supervised machine learning technique, namely Support Vector Machines. Unfortunately, SVMs have a serious drawback. They are sensitive to imbalanced datasets, outliers and high multicollinearity among training samples, which could be a cause of preferencing one group over another. Thus, an additional calibration could be required for balancing a predictive relevance of models. As a technique for this balancing, we propose the Platt’s scaling. The achieved results were demonstrated on single-target models trained on datasets exported from the ExCAPE database. Unlike traditional used machine techniques, we focus on decreasing uncertainty employing deterministic solvers.
Trvalý link: https://hdl.handle.net/11104/0336802