An Empirical Study to Analyse The Effect of Bagging and Feature Subspacing on The Performance of A Custom Ensemble Algorithm for Predicting Drug Protein Interactions

Harshita Bhargava, Amita Sharma, Prashanth Suravajhala
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Abstract

Objectives: The objective of this study is to analyse the effect of bagging and feature subspacing on the performance of a custom ensemble of decision tree classifiers for predicting drug protein interactions. Methods: In our present work we have designed a custom ensemble algorithm with decision trees as the base learner. We analysed the effect of bagging negative samples and feature subspacing on the performance of the custom ensemble in terms of AUCROC and AUPR. The Enzyme dataset from the Yamanishi dataset composed of 445 drugs and 664 proteins was used for the experiments. Findings: It was observed that the effect of bagging negative samples was significant as compared to feature supspacing in terms of AUPR metric. Now since AUPR is a metric that remains unaffected by the presence of negative samples hence the increase in AUPR by increasing the negative to positive ratio clearly indicated that the negative samples do contain the positives which are unknown and are yet to be verified. Novelty: The results give a strong indication that that feature subspacing has no considerable impact on the AUCROC metric performance of the custom ensemble while AUPR metric increases as the negative to positive ratio increases. The results give a foundation to the fact that, finding reliable negative samples from the entire set of negative drug protein pairs can further enhance the performance of the machine learning classifiers. Keywords: Decision tree classifier, Ensemble classifier, Drug discovery, Bagging, Drug repurposing
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一项实证研究:分析 Bagging 和特征 Subspacing 对用于预测药物蛋白质相互作用的自定义集合算法性能的影响
研究目的本研究的目的是分析袋化和特征子间距对用于预测药物蛋白质相互作用的决策树分类器自定义集合性能的影响。研究方法在本研究中,我们设计了一种以决策树为基础学习器的自定义集合算法。我们从 AUCROC 和 AUPR 的角度分析了袋装负样本和特征子间距对自定义集合性能的影响。实验使用了由 445 种药物和 664 个蛋白质组成的 Yamanishi 数据集中的酶数据集。实验结果观察发现,就 AUPR 指标而言,与特征超距相比,对负样本进行分组的效果显著。由于 AUPR 是一个不受阴性样本影响的指标,因此通过增加阴性样本与阳性样本的比例来提高 AUPR,这清楚地表明阴性样本中确实含有未知的阳性样本,而且这些阳性样本还有待验证。新颖性:结果有力地表明,特征子间距对自定义集合的 AUCROC 指标性能没有太大影响,而 AUPR 指标则随着负正比例的增加而增加。这些结果为以下事实提供了依据:从整个药物蛋白质负对集合中找到可靠的负样本可以进一步提高机器学习分类器的性能。关键词决策树分类器 集合分类器 药物发现 Bagging 药物再利用
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