Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning

Over the past years, large consortia have been established to fuel the sequencing of whole genomes of many cancer patients. Despite the increased abundance in tools to study the impact of SNVs, non-coding SVs have been largely ignored in these data. Here, we introduce svMIL2, an improved version of...

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Detalles Bibliográficos
Autores principales: Nieboer, Marleen M., Nguyen, Luan, de Ridder, Jeroen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277903/
https://www.ncbi.nlm.nih.gov/pubmed/34257393
http://dx.doi.org/10.1038/s41598-021-93917-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277903/
https://www.ncbi.nlm.nih.gov/pubmed/34257393
http://dx.doi.org/10.1038/s41598-021-93917-y

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