Single-cell data combined with phenotypes improves variant interpretation

Abstract Background Whole genome sequencing offers significant potential to improve the diagnosis and treatment of rare diseases by enabling the identification of thousands of rare, potentially pathogenic variants. Existing variant prioritisation tools can be complemented by approaches that incorpor...

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Bibliografiske detaljer

Principais autores:	Timothy Chapman, Timo Lassmann
Format:	Artigo
Sprog:	Inglês
Udgivet:	BMC 2025-05-01
Serier:	BMC Genomics
Fag:	Rare disease Variant prioritisation Machine learning Random forest Interpretable models Whole Genome sequencing
Online adgang:	https://doi.org/10.1186/s12864-025-11711-w
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