CI-SpliceAI—Improving machine learning predictions of disease causing splicing variants using curated alternative splice sites

BACKGROUND: It is estimated that up to 50% of all disease causing variants disrupt splicing. Due to its complexity, our ability to predict which variants disrupt splicing is limited, meaning missed diagnoses for patients. The emergence of machine learning for targeted medicine holds great potential...

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Detalles Bibliográficos
Autores principales: Strauch, Yaron, Lord, Jenny, Niranjan, Mahesan, Baralle, Diana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9165884/
https://www.ncbi.nlm.nih.gov/pubmed/35657932
http://dx.doi.org/10.1371/journal.pone.0269159

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9165884/
https://www.ncbi.nlm.nih.gov/pubmed/35657932
http://dx.doi.org/10.1371/journal.pone.0269159

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