Cargando…
A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization
Genomic variant interpretation is a critical step of the diagnostic procedure, often supported by the application of tools that may predict the damaging impact of each variant or provide a guidelines-based classification. We propose the application of Machine Learning methodologies, in particular Pe...
Autores principales: | Nicora, Giovanna, Zucca, Susanna, Limongelli, Ivan, Bellazzi, Riccardo, Magni, Paolo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8847497/ https://www.ncbi.nlm.nih.gov/pubmed/35169226 http://dx.doi.org/10.1038/s41598-022-06547-3 |
Ejemplares similares
-
Modeling the ACMG/AMP Variant Classification Guidelines as a Bayesian Classification Framework
por: Tavtigian, Sean V., et al.
Publicado: (2018) -
Sherloc: a comprehensive refinement of the ACMG–AMP variant classification criteria
por: Nykamp, Keith, et al.
Publicado: (2017) -
MAGI-ACMG: Algorithm for the Classification of Variants According to ACMG and ACGS Recommendations
por: Cristofoli, Francesca, et al.
Publicado: (2023) -
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines
por: Ghosh, Rajarshi, et al.
Publicado: (2017) -
Correction: Sherloc: a comprehensive refinement of the ACMG–AMP
variant classification criteria
por: Nykamp, Keith, et al.
Publicado: (2019)