Cargando…
Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation
High-throughput prioritization of cancer-causing mutations (drivers) is a key challenge of cancer genome projects, due to the number of somatic variants detected in tumors. One important step in this task is to assess the functional impact of tumor somatic mutations. A number of computational method...
Autores principales: | Gonzalez-Perez, Abel, Deu-Pons, Jordi, Lopez-Bigas, Nuria |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4064314/ https://www.ncbi.nlm.nih.gov/pubmed/23181723 http://dx.doi.org/10.1186/gm390 |
Ejemplares similares
-
OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations
por: Mularoni, Loris, et al.
Publicado: (2016) -
Functional impact bias reveals cancer drivers
por: Gonzalez-Perez, Abel, et al.
Publicado: (2012) -
Rational design of cancer gene panels with OncoPaD
por: Rubio-Perez, Carlota, et al.
Publicado: (2016) -
IntOGen-mutations identifies cancer drivers across tumor types
por: Gonzalez-Perez, Abel, et al.
Publicado: (2013) -
Comprehensive identification of mutational cancer driver genes across 12 tumor types
por: Tamborero, David, et al.
Publicado: (2013)