Detailed modeling of positive selection improves detection of cancer driver genes
Finding driver genes sheds lights on the biological mechanisms propelling the development of a tumour, and can suggest therapeutic strategies. Here, the authors develop driverMAPS, a model-based approach to identify driver genes, and apply it to TCGA datasets.
Saved in:
Main Authors: | Siming Zhao, Jun Liu, Pranav Nanga, Yuwen Liu, A. Ercument Cicek, Nicholas Knoblauch, Chuan He, Matthew Stephens, Xin He |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2019
|
Subjects: | |
Online Access: | https://doaj.org/article/e2cfc4a4bb3344c3b518ce9ae9dea3be |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Design of Medical Image Detail Enhancement Algorithm for Ankle Joint Talar Osteochondral Injury
by: Yundong Liu, et al.
Published: (2021) -
Genome-wide estimation of recombination, mutation and positive selection enlightens diversification drivers of Mycobacterium bovis
by: Ana C. Reis, et al.
Published: (2021) -
Adaptive colour restoration and detail retention for image enhancement
by: Kangjian He, et al.
Published: (2021) -
Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines
by: Yi Wang, et al.
Published: (2021) -
Remote sensing phenology of two Chinese northern Sphagnum bogs under climate drivers during 2001 and 2018
by: Yuwen Pang, et al.
Published: (2021)