Cargando…
MARGINAL: An Automatic Classification of Variants in BRCA1 and BRCA2 Genes Using a Machine Learning Model
Implementation of next-generation sequencing (NGS) for the genetic analysis of hereditary diseases has resulted in a vast number of genetic variants identified daily, leading to inadequate variant interpretation and, consequently, a lack of useful clinical information for treatment decisions. Herein...
Autores principales: | Karalidou, Vasiliki, Kalfakakou, Despoina, Papathanasiou, Athanasios, Fostira, Florentia, Matsopoulos, George K. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687470/ https://www.ncbi.nlm.nih.gov/pubmed/36358902 http://dx.doi.org/10.3390/biom12111552 |
Ejemplares similares
-
CHEK2 Pathogenic Variants in Greek Breast Cancer Patients: Evidence for Strong Associations with Estrogen Receptor Positivity, Overuse of Risk-Reducing Procedures and Population Founder Effects
por: Apostolou, Paraskevi, et al.
Publicado: (2021) -
A Paternally Inherited BRCA1 Mutation Associated with an Unusual Aggressive Clinical Phenotype
por: Fostira, Florentia, et al.
Publicado: (2014) -
Gene-specific machine learning for pathogenicity prediction of rare BRCA1 and BRCA2 missense variants
por: Kang, Moonjong, et al.
Publicado: (2023) -
Consistency of BRCA1 and BRCA2 Variant Classifications Among Clinical Diagnostic Laboratories
por: Lincoln, Stephen E., et al.
Publicado: (2017) -
BRCA Challenge: BRCA Exchange as a global resource for variants in BRCA1 and BRCA2
por: Cline, Melissa S., et al.
Publicado: (2018)