Cargando…
Computational Prediction of the Pathogenic Status of Cancer-Specific Somatic Variants
In-silico classification of the pathogenic status of somatic variants is shown to be promising in promoting the clinical utilization of genetic tests. Majority of the available classification tools are designed based on the characteristics of germline variants or the combination of germline and soma...
Autores principales: | Feizi, Nikta, Liu, Qian, Murphy, Leigh, Hu, Pingzhao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8804317/ https://www.ncbi.nlm.nih.gov/pubmed/35116056 http://dx.doi.org/10.3389/fgene.2021.805656 |
Ejemplares similares
-
Association Analysis of Somatic Copy Number Alteration Burden With Breast Cancer Survival
por: Zhang, Linfan, et al.
Publicado: (2018) -
Identification of significantly mutated subnetworks in the breast cancer genome
por: Ajwad, Rasif, et al.
Publicado: (2021) -
Gene-specific machine learning model to predict the pathogenicity of BRCA2 variants
por: Khandakji, Mohannad N., et al.
Publicado: (2022) -
Predicting Stage-Specific Recurrent Aberrations From Somatic Copy Number Dataset
por: Aouiche, Chaima, et al.
Publicado: (2020) -
Insights on variant analysis in silico tools for pathogenicity prediction
por: Garcia, Felipe Antonio de Oliveira, et al.
Publicado: (2022)