Enhanced Prediction for Observed Peptide Count in Protein Mass Spectrometry Data by Optimally Balancing the Training Dataset
Abstract
Imbalanced dataset affects the learning of classifiers. This imbalance problem is almost ubiquitous in biological datasets. Resampling is one of the common methods to deal with the imbalanced dataset problem. In this study, we explore the learning performance by varying the balancing ratios of training datasets, consisting of the observed peptides and absent peptides in the Mass Spectrometry experiment on the different machine learning algorithms. It has been observed that the ideal balancing ratio has yielded better performance than the imbalanced dataset, but it was not the best as compared to some intermediate ratio. By experimenting using Synthetic Minority Oversampling Technique (SMOTE) at different balancing ratios, we obtained the best results by achieving sensitivity of 92.1%, specificity value of 94.7%, overall accuracy of 93.4%, MCC of 0.869, and AUC of 0.982 with boosted random forest algorithm. This study also identifies the most discriminating features by applying the feature ranking algorithm. From the results of current experiments, it can be inferred that the performance of machine learning algorithms for the classification tasks can be enhanced by selecting optimally balanced training dataset, which can be obtained by suitably modifying the class distribution.
