Abstract
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 Learning 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 detection of pathogenic single nucleotide variants in the non-coding regions of the human genome.
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Code and Data Availability
ParSMURF-NG is distributed as source code, and it is available at https://github.com/AnacletoLAB/parSMURF-NG
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Acknowledgements
This study has been developed and performed in the context of the project “ParBigMen: ParSMURF application to Big genomic and epigenomic data for the detection of pathogenic variants in Mendelian diseases”. This project had been awarded by the Partnership for Advanced Computing in Europe in its 21st Call for Proposal. We acknowledge PRACE for awarding us access to SuperMUC-NG at LRZ, Germany.
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Petrini, A. et al. (2022). ParSMURF-NG: A Machine Learning High Performance Computing System for the Analysis of Imbalanced Big Omics Data. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 652. Springer, Cham. https://doi.org/10.1007/978-3-031-08341-9_34
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DOI: https://doi.org/10.1007/978-3-031-08341-9_34
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