School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
School of Mathematical Science, Heilongjiang University, Harbin 150080, China.
Int J Mol Sci. 2019 Aug 22;20(17):4102. doi: 10.3390/ijms20174102.
Identifying new indications for existing drugs may reduce costs and expedites drug development. Drug-related disease predictions typically combined heterogeneous drug-related and disease-related data to derive the associations between drugs and diseases, while recently developed approaches integrate multiple kinds of drug features, but fail to take the diversity implied by these features into account. We developed a method based on non-negative matrix factorization, DivePred, for predicting potential drug-disease associations. DivePred integrated disease similarity, drug-disease associations, and various drug features derived from drug chemical substructures, drug target protein domains, drug target annotations, and drug-related diseases. Diverse drug features reflect the characteristics of drugs from different perspectives, and utilizing the diversity of multiple kinds of features is critical for association prediction. The various drug features had higher dimensions and sparse characteristics, whereas DivePred projected high-dimensional drug features into the low-dimensional feature space to generate dense feature representations of drugs. Furthermore, DivePred's optimization term enhanced diversity and reduced redundancy of multiple kinds of drug features. The neighbor information was exploited to infer the likelihood of drug-disease associations. Experiments indicated that DivePred was superior to several state-of-the-art methods for prediction drug-disease association. During the validation process, DivePred identified more drug-disease associations in the top part of prediction result than other methods, benefitting further biological validation. Case studies of acetaminophen, ciprofloxacin, doxorubicin, hydrocortisone, and ampicillin demonstrated that DivePred has the ability to discover potential candidate disease indications for drugs.
鉴定现有药物的新适应症可能会降低成本并加快药物开发的速度。药物相关疾病的预测通常将异质的药物相关和疾病相关数据结合起来，以得出药物与疾病之间的关联，而最近开发的方法则整合了多种药物特征，但未能考虑到这些特征所隐含的多样性。我们开发了一种基于非负矩阵分解的方法 DivePred，用于预测潜在的药物-疾病关联。DivePred 集成了疾病相似性、药物-疾病关联以及从药物化学子结构、药物靶标蛋白结构域、药物靶标注释和与药物相关的疾病中提取的各种药物特征。不同的药物特征从不同的角度反映了药物的特征，利用多种特征的多样性对于关联预测至关重要。各种药物特征具有较高的维度和稀疏的特征，而 DivePred 将高维药物特征投影到低维特征空间中，生成药物的密集特征表示。此外，DivePred 的优化项增强了多种药物特征的多样性并减少了冗余。利用邻居信息推断药物-疾病关联的可能性。实验表明，DivePred 在预测药物-疾病关联方面优于几种最先进的方法。在验证过程中，DivePred 在预测结果的前半部分比其他方法识别出更多的药物-疾病关联，有利于进一步的生物学验证。对乙酰氨基酚、环丙沙星、多柔比星、氢化可的松和氨苄西林的案例研究表明，DivePred 有能力发现药物的潜在候选疾病适应症。
