Department of Computer Science, Wayne State University, Detroit, MI, USA.
Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, USA.
Bioinformatics. 2019 Oct 1;35(19):3672-3678. doi: 10.1093/bioinformatics/btz156.
Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs.
We validated our framework by showing that the proposed method incorporating distance metric learning technique can retrieve FDA-approved drugs for their approved indications. Once validated, we used our approach to identify a few strong candidates for repurposing.
The R scripts are available on demand from the authors.
Supplementary data are available at Bioinformatics online.
药物重定位是经典药物发现途径的一种潜在替代方法。重定位涉及为已批准的药物寻找新的适应症。在这项工作中，我们提出了一种新的基于机器学习的药物重定位方法。该方法探索了药物与疾病之间的反相似性，以发现药物的新用途。更具体地说，我们提出的方法考虑了三种信息来源：（i）用小分子处理的人类细胞系的大规模基因表达谱，（ii）人类疾病的基因表达谱和（iii）美国食品和药物管理局 (FDA) 批准的药物和疾病之间的已知关系。利用这些数据，我们提出的方法通过基于监督机器学习的算法学习相似性度量，使得疾病及其相关的 FDA 批准药物的距离小于其他疾病-药物对的距离。
我们通过证明结合距离度量学习技术的建议方法可以检索 FDA 批准的药物用于其批准的适应症来验证我们的框架。验证后，我们使用我们的方法来确定一些重新定位的强候选者。
可根据作者的要求提供 R 脚本。
补充数据可在“Bioinformatics”在线获取。
