Machine learning prediction of side effects for drugs in clinical trials

Resumo

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.

Ficha do documento

Tipo

Outro

Ano

2022

Instituição

Cell Reports Methods

Fonte

Repositório da FGV

Idioma

Inglês

Acesso

Não informado

Identificador

oai:repositorio.fgv.br:10438/33991

Temas

Tecnologia

Palavras-chave

Aprendizagem de máquinaInteligência artificialDrogas terapêuticasMatemática