2023
DOI: 10.1101/2023.10.02.560464
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Digenic variant interpretation with hypothesis-driven explainable AI

Federica De Paoli,
Giovanna Nicora,
Silvia Berardelli
et al.

Abstract: MotivationThe digenic inheritance hypothesis holds the potential to enhance diagnostic yield in rare diseases. Computational approaches capable of accurately interpreting and prioritizing digenic combinations based on the proband’s phenotypic profiles and familial information can provide valuable assistance to clinicians during the diagnostic process.ResultsWe have developed diVas, a hypothesis-driven machine learning approach that can effectively interpret genomic variants across different gene pairs. DiVas d… Show more

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Cited by 2 publications
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References 56 publications
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