IEEE/ACM Trans Comput Biol Bioinform. 2019 Jan-Feb;16(1):154-162. doi: 10.1109/TCBB.2018.2830384. Epub 2018 Apr 26.
Drug repositioning, i.e., identifying new indications for known drugs, has attracted a lot of attentions recently and is becoming an effective strategy in drug development. In literature, several computational approaches have been proposed to identify potential indications of old drugs based on various types of data sources. In this paper, by formulating the drug-disease associations as a low-rank matrix, we propose a novel method, namely DrPOCS, to identify candidate indications of old drugs based on projection onto convex sets (POCS). With the integration of drug structure and disease phenotype information, DrPOCS predicts potential associations between drugs and diseases with matrix completion. Benchmarking results demonstrate that our proposed approach outperforms popular existing approaches with high accuracy. In addition, a number of novel predicted indications are validated with various types of evidences, indicating the predictive power of our proposed approach.
药物重定位，即确定已知药物的新适应症，最近引起了很多关注，并且正在成为药物开发的一种有效策略。在文献中，已经提出了几种基于各种类型数据源的计算方法来识别旧药物的潜在适应症。在本文中，通过将药物-疾病关联表示为低秩矩阵，我们提出了一种新方法，即 DrPOCS，该方法基于凸集投影（POCS）来识别旧药物的候选适应症。通过整合药物结构和疾病表型信息，DrPOCS 可以通过矩阵完成来预测药物和疾病之间的潜在关联。基准测试结果表明，我们提出的方法具有高精度，优于现有的流行方法。此外，还通过各种类型的证据验证了一些新的预测适应症，表明了我们提出的方法的预测能力。
