Scoring multiple features to predict drug disease associations using information fusion and aggregation

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-疾病关联预测是当前药物重定位（drug repositioning）领域的重要分支，亦是药学科学中极具挑战性的研究课题。现有多种计算方法采用基于网络的方法与机器学习（machine learning）手段，针对新适应症重定位已上市老药。然而，此类方法往往忽略药物与疾病的特征，以及各特征、特征间关联或交互作用的优先级与重要性，同时未考虑不确定性程度。在预测未知药物-疾病相互作用时，可获取的多源数据与多样特征能够为提升预测结果的准确性与可靠性提供支撑，可通过数据融合（data fusion）方法与聚合算子（aggregation operator）对这些信息进行联合挖掘。因此，可借助特征融合方法构建高阶特征。为此，本研究提出一种名为评分均值核融合（scored mean kernel fusion, SMKF）的计算方法，该方法针对平均聚合算子提出一种全新的评分机制，称之为评分均值（scored mean）。为预测新型药物适应症，该方法在两个层级上系统性融合与药物或疾病相关的多类特征：药物-药物层级与药物-疾病层级。本研究旨在探究药物与疾病特征以及数据融合策略对药物-疾病相互作用预测的影响。本方法通过权威的药物-疾病金标准数据集（gold-standard dataset）进行了验证。与现有方法相比，所提方法性能更优，其覆盖曲线下面积（area under cover, AUC）达0.91、F测度（F-measure）为84.9%、马修斯相关系数（Matthews correlation coefficient, MCC）为70.31%，展现出极具竞争力的预测性能。