Moghadam H, Rahgozar M, Gharaghani S
a DBRG, CIPCE, School of Electrical and Computer Engineering, College of Engineering , University of Tehran , Tehran , Iran.
b LBD, Institute of Biochemistry and Biophysics , University of Tehran , Tehran , Iran.
SAR QSAR Environ Res. 2016 Aug;27(8):609-28. doi: 10.1080/1062936X.2016.1209241. Epub 2016 Jul 25.
Prediction of drug-disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug-disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results. This information can be collectively mined using data fusion methods and aggregation operators. Therefore, we can use the feature fusion method to make high-level features. We have proposed a computational method named scored mean kernel fusion (SMKF), which uses a new method to score the average aggregation operator called scored mean. To predict novel drug indications, this method systematically combines multiple features related to drugs or diseases at two levels: the drug-drug level and the drug-disease level. The purpose of this study was to investigate the effect of drug and disease features as well as data fusion to predict drug-disease interactions. The method was validated against a well-established drug-disease gold-standard dataset. When compared with the available methods, our proposed method outperformed them and competed well in performance with area under cover (AUC) of 0.91, F-measure of 84.9% and Matthews correlation coefficient of 70.31%.
药物-疾病关联预测是药物重新定位领域当前的研究方向之一，已成为药学领域一个具有挑战性的课题。几种现有的计算方法使用基于网络和机器学习的方法将旧药重新定位用于新的适应症。然而，它们常常忽略药物和疾病的特征以及每个特征、关系或特征之间相互作用的优先级和重要性，以及不确定性程度。在预测未知的药物-疾病相互作用时，有多种数据源和多个特征可供使用，这些能够提供更准确和可靠的结果。可以使用数据融合方法和聚合算子来共同挖掘这些信息。因此，我们可以使用特征融合方法来生成高级特征。我们提出了一种名为评分均值核融合（SMKF）的计算方法，该方法使用一种新的方法对称为评分均值的平均聚合算子进行评分。为了预测新的药物适应症，该方法在药物-药物水平和药物-疾病水平两个层面上系统地结合了与药物或疾病相关的多个特征。本研究的目的是研究药物和疾病特征以及数据融合对预测药物-疾病相互作用的影响。该方法针对一个成熟的药物-疾病金标准数据集进行了验证。与现有方法相比，我们提出的方法表现更优，在性能上与0.91的覆盖面积下的曲线（AUC）、84.9%的F值和70.31%的马修斯相关系数竞争良好。
