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The sophistication of the available technologies is ever-increasing, yet, manual candidate refinement is still a necessary step in state-of-the-art processing pipelines. This limits reproducibility and introduces a bottleneck with respect to scalability. We demonstrate that the validation of genetic variants can be improved using a machine learning approach resting on a Convolutional Neural Network, trained using existing human annotation. In contrast to existing approaches, we introduce a way in which contextual data from sequencing tracks can be included into the automated assessment. 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Sequencing data used in this study were either accessed from repositories or generated from biobanked mouse samples that derived from mouse experiments unrelated to this study and that had been performed in accordance with the relevant guidelines and regulations, in particular the directive 2010\/63\/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes. The mouse experiments whose results made up the in-house dataset had been performed under the approval of the Austrian animal ethics committee (BMWF 66.012\/0009-II\/3b\/2012, TGV\/52\/11-2012 and BMBWF\n                      \n                      66.012\/0002-V\/3b\/2018).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing\u00a0 interests"}}],"article-number":"158"}}