To assist drug development, many computational methods have been proposed to identify potential drug-disease treatment associations before wet experiments. Based on the assumption that similar drugs may treat similar diseases, most methods calculate the similarities of drugs and diseases by using various chemical or biological features. However, since these features may be unknown or hard to collect, such methods will not work in the face of incomplete data. Besides, due to the lack of validated negative samples in the drug-disease associations data, most methods have no choice but to simply select some unlabeled samples as negative ones, which may introduce noises and decrease the reliability of prediction. Herein, we propose a new method (TS-SVD) which only uses those known drug-protein, disease-protein and drug-disease interactions to predict the potential drug-disease associations. In a constructed drug-protein-disease heterogeneous network, assuming that drugs/diseases relating to some common proteins or diseases/drugs may be similar, we get the common neighbors count matrix of drugs/diseases, then convert it to a topological similarity matrix. After that, we get low dimensional embedding representations of drug-disease pairs by using topological features and singular value decomposition. Finally, a Random Forest classifier is trained to do the prediction. To train a more reasonable model, we select out some reliable negative samples based on the ${k}$ -step neighbors relationships between drugs and diseases. Compared with some state-of-the-art methods, we use less information but achieve better or comparable performance. Meanwhile, our strategy for selecting reliable negative samples can improve the performances of these methods. Case studies have further shown the practicality of our method in discovering novel drug-disease associations.

中文翻译：

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仅链接预测与交互作用数据及其在药物重新定位中的应用。

为了辅助药物开发，已经提出了许多计算方法来在湿实验之前鉴定潜在的药物疾病治疗关联。基于类似药物可能治疗相似疾病的假设，大多数方法都通过使用各种化学或生物学特征来计算药物和疾病的相似性。但是，由于这些功能可能未知或难以收集，因此面对不完整的数据，此类方法将不起作用。此外，由于在药物-疾病关联数据中缺少经过验证的阴性样本，大多数方法别无选择，只能选择一些未标记的样本作为阴性样本，这可能会引入噪声并降低预测的可靠性。在此，我们提出一种仅使用已知药物蛋白的新方法（TS-SVD），疾病-蛋白质和药物-疾病相互作用，以预测潜在的药物-疾病关联。在一个构建的药物-蛋白质-疾病异质网络中，假设与某些常见蛋白质或疾病/药物有关的药物/疾病可能相似，我们得到药物/疾病的共同邻居计数矩阵，然后将其转换为拓扑相似性矩阵。之后，我们利用拓扑特征和奇异值分解得到药物-疾病对的低维嵌入表示。最后，训练一个随机森林分类器进行预测。为了训练更合理的模型，我们根据 假设与某些常见蛋白质或疾病/药物有关的药物/疾病可能相似，我们得到药物/疾病的共同邻居计数矩阵，然后将其转换为拓扑相似性矩阵。之后，我们利用拓扑特征和奇异值分解得到药物-疾病对的低维嵌入表示。最后，训练一个随机森林分类器进行预测。为了训练更合理的模型，我们根据 假设与某些常见蛋白质或疾病/药物有关的药物/疾病可能相似，我们得到药物/疾病的共同邻居计数矩阵，然后将其转换为拓扑相似性矩阵。之后，我们利用拓扑特征和奇异值分解得到药物-疾病对的低维嵌入表示。最后，训练一个随机森林分类器进行预测。为了训练更合理的模型，我们根据 $ {k} $ 毒品与疾病之间的近邻关系。与某些最新方法相比，我们使用的信息更少，但是却获得了更好或可比的性能。同时，我们选择可靠的阴性样品的策略可以改善这些方法的性能。案例研究进一步表明了我们的方法在发现新型药物-疾病关联中的实用性。