Cargando…
AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes
The current challenge in cancer research is to increase the resolution of driver prediction from gene-level to mutation-level, which is more closely aligned with the goal of precision cancer medicine. Improved methods to distinguish drivers from passengers are urgently needed to dig out driver mutat...
Autores principales: | Wang, Haoxuan, Wang, Tao, Zhao, Xiaolu, Wu, Honghu, You, Mingcong, Sun, Zhongsheng, Mao, Fengbiao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671397/ https://www.ncbi.nlm.nih.gov/pubmed/33575629 http://dx.doi.org/10.1093/nargab/lqaa084 |
Ejemplares similares
-
OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers
por: Wang, Tao, et al.
Publicado: (2020) -
Comprehensive evaluation of computational methods for predicting cancer driver genes
por: Shi, Xiaohui, et al.
Publicado: (2022) -
DriverGenePathway: Identifying driver genes and driver pathways in cancer based on MutSigCV and statistical methods
por: Xu, Xiaolu, et al.
Publicado: (2023) -
A Bayesian Framework to Identify Methylcytosines from High-Throughput Bisulfite Sequencing Data
por: Xie, Qing, et al.
Publicado: (2014) -
Eyes can tell: Assessment of implicit attitudes toward AI art
por: Zhou, Yizhen, et al.
Publicado: (2023)