Department of Electronics and Mechatronics Engineering, Facultad de Ingeniería, Universidad Nacional de Asunción, San Lorenzo, Paraguay.
School of Applied Mathematics, Fundação Getulio Vargas, Rio de Janeiro, Brazil.
Cell Rep Methods. 2022 Dec 7;2(12):100358. doi: 10.1016/j.crmeth.2022.100358. eCollection 2022 Dec 19.
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.
早期准确地检测副作用对于开发中的药物的临床成功至关重要。在这里，我们旨在预测在随机对照临床试验中确定的少数副作用的药物的未知副作用。我们的机器学习框架，几何自表达模型（GSEM），从药理学图网络中学习药物和副作用的全局最优自表示。我们展示了 GSEM 在 505 种治疗多样性药物和 904 种来自多个人体生理系统的副作用上的有效性。在这里，我们还展示了一种数据集成策略，该策略可以被采用来提高副作用预测模型识别可能仅在药物进入市场后才出现的未知副作用的能力。
