Supplementary data for "Algorithm-level data-guided correction for class imbalance in biological machine learning predictions: Protein interactions as a case"

In real-world biomedical applications of data mining, machine learning and artificial intelligence, there are situations where the widespread problem of class imbalance cannot be addressed by data-level methods such as over- or under-sampling. Correct and efficient use of algorithm-level methods, on the other hand, needs paying heed to data structure and content. This study aims to devise and examine simple methods for addressing the imbalanced class distribution issue in predicting the protein-protein interaction (PPI) sites in membrane proteins as a biomedical case experiment. Using an adopted dataset of membrane protein complexes and a retrieved validation set, a class-weighted random forests (CWRF) classifier model was built for predicting interfacial residues from positional frequencies and an evolutionary index. Among several class weighting methods, a data imbalance-emulating weighting method for the CWRF model achieved an area under the receiver operating characteristics curve (AUC) of 0.815 (95% CI: 0.805-0.823) in the independent test prediction and 0.802 (95% CI: 0.794-0.809) in the prediction for the external validation set, which outperformed previous similar studies. A case prediction confirmed the practical utility of this method. The proposed approach implies potential applications in other fields of biomedicine and beyond. It also highlights the role of algorithm-data interplay in addressing the class imbalance.

在数据挖掘、机器学习和人工智能的实际生物医学应用中，存在一类普遍问题——类别不平衡，无法通过过采样或欠采样等数据层面方法解决的情况。另一方面，算法层面方法的正确高效应用需关注数据的结构与内容。本研究旨在设计并验证若干简易方法，以解决膜蛋白中蛋白质-蛋白质相互作用（PPI）位点预测任务中的类别分布不平衡问题，并将其作为生物医学案例展开实验。研究采用膜蛋白复合物的适配数据集及检索得到的验证集，构建了类别加权随机森林（CWRF）分类器模型，用于基于位置频率和进化指数预测界面残基。在多种类别加权方法中，针对CWRF模型的一种模拟数据不平衡的加权方法表现最优：独立测试预测的受试者工作特征曲线下面积（AUC）达0.815（95%置信区间：0.805-0.823），外部验证集预测的AUC为0.802（95%置信区间：0.794-0.809），性能优于此前同类研究。案例预测验证了该方法的实际应用价值。本研究提出的方法在生物医学及其他领域具有潜在应用前景，同时强调了算法与数据的相互作用在解决类别不平衡问题中的关键角色。