ParSMURF-NG: A Machine Learning High Performance Computing System for the Analysis of Imbalanced Big Omics Data

In the context of Genomic and Precision Medicine, prediction problems are often characterized by a high imbalance between classes and Big Data. This requires specialized tools, as traditional Machine Learn- ing approaches may struggle with big datasets and often fail to predict the minority class with unbalanced classification problems. In this work we present ParSMURF-NG, a High Performance Computing-oriented Machine Learning approach designed to scale well on big omics data. We measured its performance capabilities on three current-generation HPC systems and we showed its usefulness in the context of Genomic Medicine, providing a powerful model for the detec- tion of pathogenic single nucleotide variants in the non-coding regions of the human genome.