Predicting Pathogenic Non-coding Variants on Imbalanced Data Set using Cluster Ensemble Sampling

In the past few years, many variants in the non-coding regions of the human genome have been reported by personal whole genome sequencing. It is a challenge to distinguish pathogenic non-coding variants from such a large number of benign non-coding variants. Many machine learning methods for predicting pathogenic non-coding variants have been proposed. However, the precision and recall rates of the currently existing methods decline rapidly when the number of negative samples in the data increases. Both under-and over-sampling techniques have been employed in the field of machine learning to resolve the poor performance of classification methods on imbalanced data. Even though, we observed that a more sophisticated method with better performance is still largely desired for the problem of predicting pathogenic non-coding variants. In this regard, this study aims at presenting a general framework for imbalanced data learning, CE-SMURF, which incorporates both Cluster Ensemble (CE) sampling and hyper-ensemble techniques to further improve the prediction accuracy of detecting pathogenic non-coding variants. The results demonstrate that the final setting of CE-SMURF (f = 0, r = 0.1) is superior in training, and outperforms other existing methods on the testing data, providing a valuable insight to tackle the imbalanced learning issue for many future applications in the field of genomic precision medicine.