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Vol. 234, Issue 6, June 2019, pp. 16-21

 

Bullet

 

Training Neural Networks with Balanced Mini-batch to Improve the Prediction of Pathogenic Genomic Variants
in Mendelian Diseases
 

Luca Cappelletti, Jessica Gliozzo, Alessandro Petrini and * Giorgio Valentini

AnacletoLab, Dipartimento di Informatica, Universit

degli Studi di Milano, Via Giovanni Celoria 18, 20133, Italy
* E-mail: valentini@di.unimi.it

 

Received: 15 May 2019 /Accepted: 15 June 2019 /Published: 30 June 2019

Digital Sensors and Sensor Sysstems

 

Abstract: Known pathogenic variants associated with genetic Mendelian diseases represent a tiny minority of the overall genetic variation that characterizes the human genome. In this context classical imbalance-aware machine learning methods are unable to distinguish pathogenic from benign variants, since they are severely biased toward the majority (benign) class. Recent works based on ensemble and hyper- ensemble methods showed that by adopting sampling techniques we can significantly improve performance on this challenging task. Inspired by these findings and by recent successful applications of deep learning to Precision Medicine, we propose two learning techniques for neural networks designed to assure a certain balancing between pathogenic and benign variants during the training phase, or to assure that with high probability at least one pathogenic variant is included in the training mini-batch set of examples. The experimental prediction of non-coding mutations associated with Mendelian diseases show the effectiveness of these proposed neural network training approaches.

 

Keywords: Neural Networks, Imbalance-aware Neural Networks, Deep Learning, Prediction of pathogenic genomic variants, Mendelian diseases.

 

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