Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs and diseases. These methods approach the problem using either machine learning- or network-based models with an assumption that similar drugs can be used for similar diseases to identify new indications of drugs. Therefore, similarities between drugs and between diseases are usually used as inputs. In addition, known drug-disease associations are also needed for the methods as prior information. It should be noted that those associations are still not well established due to the fact that many of marketed drugs have been withdrawn and this could affect the outcome of the methods. In this study, we propose a novel method named RLSDR (Regularized Least Square for Drug Repositioning) to find new uses of drugs. More specifically, it relies on a semi-supervised learning model, Regularized Least Square, thus it does not require definition of non-drug-disease associations as previously proposed machine learning-based methods. In addition, the similarity between drugs measured by chemical structures of drug compounds and the similarity between diseases which share phenotypes can be represented in a form of either similarity network or similarity matrix as inputs of the method. Moreover, instead of using a gold-standard set of known drug-disease associations, we construct an artificial set of the associations based on known disease-gene and drug-target associations. Experiment results demonstrate that RLSDR achieves better prediction performance on the artificial set of drug-disease associations than that on the gold-standard ones in terms of area under the Receiver Operating Characteristic (ROC) curve (AUC). In addition, it outperforms two representative network-based methods irrespective of the prior information of drug-disease associations. Novel indications for a number of drugs are also identified and validated by evidences from a different data resource.

中文翻译：

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通过在半监督学习模型中整合已知疾病基因和药物靶标关联进行药物重新定位

计算药物重新定位已被证明是一种从现有药物中发现新用途的有前途且有效的策略。为了实现这一目标，已经提出了许多计算方法，这些方法基于药物和疾病的不同数据源。这些方法使用基于机器学习或基于网络的模型来解决问题，并假设类似的药物可用于类似的疾病以识别药物的新适应症。因此，药物之间和疾病之间的相似性通常用作输入。此外，这些方法还需要已知的药物-疾病关联作为先验信息。应该指出的是，由于许多上市药物已被撤回，这些关联仍未完全建立，这可能会影响方法的结果。在这项研究中，我们提出了一种名为 RLSDR（药物重新定位的正则化最小二乘法）的新方法来寻找药物的新用途。更具体地说，它依赖于半监督学习模型正则化最小二乘法，因此它不需要像以前提出的基于机器学习的方法那样定义非药物疾病关联。此外，通过药物化合物的化学结构测量的药物之间的相似性和共享表型的疾病之间的相似性可以以相似性网络或相似性矩阵的形式表示作为该方法的输入。此外，我们没有使用已知药物-疾病关联的黄金标准集，而是基于已知的疾病-基因和药物-靶标关联构建一组人工关联。实验结果表明，就受试者工作特征 (ROC) 曲线 (AUC) 下的面积而言，RLSDR 在人工药物-疾病关联集上的预测性能优于黄金标准。此外，无论药物与疾病关联的先验信息如何，它都优于两种代表性的基于网络的方法。许多药物的新适应症也通过来自不同数据资源的证据进行识别和验证。