Cargando…
LEAP: Using machine learning to support variant classification in a clinical setting
Advances in genome sequencing have led to a tremendous increase in the discovery of novel missense variants, but evidence for determining clinical significance can be limited or conflicting. Here, we present Learning from Evidence to Assess Pathogenicity (LEAP), a machine learning model that utilize...
Autores principales: | Lai, Carmen, Zimmer, Anjali D., O'Connor, Robert, Kim, Serra, Chan, Ray, van den Akker, Jeroen, Zhou, Alicia Y., Topper, Scott, Mishne, Gilad |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7317941/ https://www.ncbi.nlm.nih.gov/pubmed/32176384 http://dx.doi.org/10.1002/humu.24011 |
Ejemplares similares
-
A machine learning model to determine the accuracy of variant calls in capture-based next generation sequencing
por: van den Akker, Jeroen, et al.
Publicado: (2018) -
A scalable, aggregated genotypic–phenotypic database for human disease variation
por: Barrett, Ryan, et al.
Publicado: (2019) -
Low coverage whole genome sequencing enables accurate assessment of common variants and calculation of genome-wide polygenic scores
por: Homburger, Julian R., et al.
Publicado: (2019) -
Personalized medicine to implementation science: Thiopurines set for the leap
por: Sharma, Vishal, et al.
Publicado: (2022) -
Objective and automatic classification of Parkinson disease with Leap Motion controller
por: Butt, A. H., et al.
Publicado: (2018)