scholarly journals The association between smoking or passive smoking and cardiovascular diseases using a Bayesian hierarchical model: based on the 2008-2013 Korea Community Health Survey

2017 ◽  
Vol 39 ◽  
pp. e2017026 ◽  
Author(s):  
Whanhee Lee ◽  
Sung-Hee Hwang ◽  
Hayoung Choi ◽  
Ho Kim
Keyword(s):  
Cardiovascular Diseases ◽  
Community Health ◽  
Health Survey ◽  
Hierarchical Model ◽  
Passive Smoking ◽  
Bayesian Hierarchical Model ◽  
Bayesian Hierarchical ◽  
Model Based ◽  
Community Health Survey

scholarly journals A Bayesian model integration for mutation calling through data partitioning

2019 ◽  
Vol 35 (21) ◽  
pp. 4247-4254 ◽  
Author(s):  
Takuya Moriyama ◽  
Seiya Imoto ◽  
Shuto Hayashi ◽  
Yuichi Shiraishi ◽  
Satoru Miyano ◽  
...  
Keyword(s):  
Hierarchical Model ◽  
Bayesian Model ◽  
Information Sources ◽  
Tumor Model ◽  
Bayesian Hierarchical Model ◽  
Generative Models ◽  
Error Model ◽  
Model Integration ◽  
Bayesian Hierarchical ◽  
Model Based

Abstract Motivation Detection of somatic mutations from tumor and matched normal sequencing data has become among the most important analysis methods in cancer research. Some existing mutation callers have focused on additional information, e.g. heterozygous single-nucleotide polymorphisms (SNPs) nearby mutation candidates or overlapping paired-end read information. However, existing methods cannot take multiple information sources into account simultaneously. Existing Bayesian hierarchical model-based methods construct two generative models, the tumor model and error model, and limited information sources have been modeled. Results We proposed a Bayesian model integration framework named as partitioning-based model integration. In this framework, through introducing partitions for paired-end reads based on given information sources, we integrate existing generative models and utilize multiple information sources. Based on that, we constructed a novel Bayesian hierarchical model-based method named as OHVarfinDer. In both the tumor model and error model, we introduced partitions for a set of paired-end reads that cover a mutation candidate position, and applied a different generative model for each category of paired-end reads. We demonstrated that our method can utilize both heterozygous SNP information and overlapping paired-end read information effectively in simulation datasets and real datasets. Availability and implementation https://github.com/takumorizo/OHVarfinDer. Supplementary information Supplementary data are available at Bioinformatics online.

Bayesian hierarchical model-based prognostics for lithium-ion batteries

2018 ◽  
Vol 172 ◽  
pp. 25-35 ◽  
Author(s):  
Madhav Mishra ◽  
Jesper Martinsson ◽  
Matti Rantatalo ◽  
Kai Goebel
Keyword(s):  
Lithium Ion Batteries ◽  
Hierarchical Model ◽  
Lithium Ion ◽  
Bayesian Hierarchical Model ◽  
Bayesian Hierarchical ◽  
Model Based
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