RT info:eu-repo/semantics/article
T1 Machine learning prediction of side effects for drugs in clinical trials
A1 Galeano Galeano, Diego Ariel
A1 Paccanaro, Alberto
A2 Universidad Católica Nuestra Señora de la Asunción
A2 Cámara Paraguaya de Exportadores y Comercializadores de Cereales y Oleaginosas
A2 Centro de Ingeniería para la InvestigaciónDesarrollo e Innovación Tecnológica
AB Early and accurate detection of side effects is critical for the clinical success of drugs under development. Here, we aim to predict unknown side effects for drugs with a small number of side effects identified in randomized controlled clinical trials. Our machine learning framework, the geometric self-expressive model (GSEM), learns globally optimal self-representations for drugs and side effects from pharmacological graph networks. We show the usefulness of the GSEM on 505 therapeutically diverse drugs and 904 side effects from multiple human physiological systems. Here, we also show a data integration strategy that could be adopted to improve the ability of side effect prediction models to identify unknown side effects that might only appear after the drug enters the market.
PB Cell Press
YR 2022
FD 2022-12-07
LK http://hdl.handle.net/20.500.14066/4732
UL http://hdl.handle.net/20.500.14066/4732
LA eng
NO Galeano, D., & Paccanaro, A. (2022). Machine learning prediction of side effects for drugs in clinical trials. Cell Reports Methods, 2(12), Artículo 100358.https://doi.org/10.1016/j.crmeth.2022.100358
NO Correspondence: dgaleano@ing.una.py.
NO Consejo Nacional de Ciencia y Tecnología
DS MINDS@UW
RD 04-sep-2026