Multichannel Spatial Clustering Using Model-Based Source Separation

2017 ◽  
pp. 51-77
Author(s):  
Michael I. Mandel ◽  
Jon P. Barker
Keyword(s):  
Spatial Clustering ◽  
Source Separation ◽  
Model Based

scholarly journals Joint Mixing Vector and Binaural Model Based Stereo Source Separation

2014 ◽  
Vol 22 (9) ◽  
pp. 1434-1448 ◽  
Author(s):  
Atiyeh Alinaghi ◽  
Philip Jb Jackson ◽  
Qingju Liu ◽  
Wenwu Wang
Keyword(s):  
Source Separation ◽  
Model Based

scholarly journals Video-Aided Model-Based Source Separation in Real Reverberant Rooms

2013 ◽  
Vol 21 (9) ◽  
pp. 1900-1912 ◽  
Author(s):  
Muhammad Salman Khan ◽  
Syed Mohsen Naqvi ◽  
Ata- ur-Rehman ◽  
Wenwu Wang ◽  
Jonathon Chambers
Keyword(s):  
Source Separation ◽  
Model Based

scholarly journals Model-based analysis of positive selection significantly expands the list of cancer driver genes, including RNA methyltransferases

2018 ◽  
Author(s):  
Siming Zhao ◽  
Jun Liu ◽  
Pranav Nanga ◽  
Yuwen Liu ◽  
A. Ercument Cicek ◽  
...  
Keyword(s):  
Positive Selection ◽  
Cancer Biology ◽  
Spatial Clustering ◽  
Strong Support ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Model Based ◽  
Cancer Driver Genes ◽  
Model Based Analysis

AbstractIdentifying driver genes is a central problem in cancer biology, and many methods have been developed to identify driver genes from somatic mutation data. However, existing methods either lack explicit statistical models, or rely on very simple models that do not capture complex features in somatic mutations of driver genes. Here, we present driverMAPS (Model-based Analysis of Positive Selection), a more comprehensive model-based approach to driver gene identification. This new method explicitly models, at the single-base level, the effects of positive selection in cancer driver genes as well as highly heterogeneous background mutational process. Its selection model captures elevated mutation rates in functionally important sites using multiple external annotations, as well as spatial clustering of mutations. Its background mutation model accounts for both known covariates and unexplained local variation. Simulations under realistic evolutionary models demonstrate that driverMAPS greatly improves the power of driver gene detection over state-of-the-art approaches. Applying driverMAPS to TCGA data across 20 tumor types identified 159 new potential driver genes. Cross-referencing this list with data from external sources strongly supports these findings. The novel genes include the mRNA methytransferases METTL3-METTL14, and we experimentally validated METTL3 as a potential tumor suppressor gene in bladder cancer. Our results thus provide strong support to the emerging hypothesis that mRNA modification is an important biological process underlying tumorigenesis.

Sign in / Sign up

Export Citation Format

Share Document