Department of Computer Science, University of Northern Iowa, Cedar Falls, IA, USA.
Department of Computer Science, Hunter College, The Graduate Center, The City University of New York, New York, NY, USA.
Bioinformatics. 2018 Aug 15;34(16):2835-2842. doi: 10.1093/bioinformatics/bty193.
Adverse drug reactions (ADRs) are one of the main causes of death and a major financial burden on the world's economy. Due to the limitations of the animal model, computational prediction of serious and rare ADRs is invaluable. However, current state-of-the-art computational methods do not yield significantly better predictions of rare ADRs than random guessing.
We present a novel method, based on the theory of 'compressed sensing' (CS), which can accurately predict serious side-effects of candidate and market drugs. Not only is our method able to infer new chemical-ADR associations using existing noisy, biased and incomplete databases, but our data also demonstrate that the accuracy of CS in predicting a serious ADR for a candidate drug increases with increasing knowledge of other ADRs associated with the drug. In practice, this means that as the candidate drug moves up the different stages of clinical trials, the prediction accuracy of our method will increase accordingly.
The program is available at https://github.com/poleksic/side-effects.
Supplementary data are available at Bioinformatics online.
药物不良反应 (ADRs) 是导致死亡的主要原因之一，也是全球经济的主要财政负担。由于动物模型的局限性，严重和罕见 ADR 的计算预测非常有价值。然而，目前最先进的计算方法并不能比随机猜测更准确地预测罕见的 ADR。
我们提出了一种新方法，基于“压缩感知” (CS) 的理论，该方法可以准确预测候选药物和市场药物的严重副作用。我们的方法不仅能够利用现有嘈杂、有偏差和不完整的数据库推断新的化学-ADR 关联，而且我们的数据还表明，CS 在预测候选药物的严重 ADR 方面的准确性随着与药物相关的其他 ADR 知识的增加而增加。实际上，这意味着随着候选药物在临床试验的不同阶段推进，我们方法的预测准确性将相应提高。
该程序可在 https://github.com/poleksic/side-effects 上获得。
补充数据可在 Bioinformatics 在线获得。
