A neural model for the prediction of pathogenic genomic variants in Mendelian diseases

The detection of pathogenic genomic variants associated with genetic or cancer diseases represents an open problem in the context of the Genomic Medicine. In particular the detection of mutations in the non-coding regions of human genome represents a particularly challenging machine learning problem, since the number of neutral variants largely outnumber the pathogenic ones, thus resulting in highly imbalanced classification problems. We applied neural networks to the detection of pathogenic regulatory genomic variants in Mendelian diseases and we showed that leveraging imbalance-aware techniques and deep learning algorithms, we can obtain state-of-the-art results, using a less complex model than those proposed in literature for this challenging prediction task.