scholarly journals The reciprocal Bayesian LASSO

2021 ◽  
Author(s):  
Himel Mallick ◽  
Rahim Alhamzawi ◽  
Erina Paul ◽  
Vladimir Svetnik
Keyword(s):  
Bayesian Lasso

A new Gibbs sampler for Bayesian lasso

2018 ◽  
Vol 49 (7) ◽  
pp. 1855-1871 ◽  
Author(s):  
Rahim Alhamzawi ◽  
Haithem Taha Mohammad Ali
Keyword(s):  
Gibbs Sampler ◽  
Bayesian Lasso

scholarly journals PREDICTIVE MODEL SELECTION CRITERIA FOR BAYESIAN LASSO REGRESSION

2015 ◽  
Vol 28 (1) ◽  
pp. 67-82 ◽  
Author(s):  
Shuichi Kawano ◽  
Ibuki Hoshina ◽  
Kaito Shimamura ◽  
Sadanori Konishi
Keyword(s):  
Model Selection ◽  
Predictive Model ◽  
Selection Criteria ◽  
Lasso Regression ◽  
Bayesian Lasso ◽  
Model Selection Criteria

scholarly journals Single-Trait and Multiple-Trait Genomic Prediction From Multi-Class Bayesian Alphabet Models Using Biological Information

2021 ◽  
Vol 12 ◽  
Author(s):  
Zigui Wang ◽  
Hao Cheng
Keyword(s):  
Molecular Markers ◽  
Genomic Prediction ◽  
Simulated Data ◽  
Software Tool ◽  
Biological Information ◽  
Superior Performance ◽  
Multiple Trait ◽  
Bayesian Lasso ◽  
Trait Analysis ◽  
Causal Variants

Genomic prediction has been widely used in multiple areas and various genomic prediction methods have been developed. The majority of these methods, however, focus on statistical properties and ignore the abundant useful biological information like genome annotation or previously discovered causal variants. Therefore, to improve prediction performance, several methods have been developed to incorporate biological information into genomic prediction, mostly in single-trait analysis. A commonly used method to incorporate biological information is allocating molecular markers into different classes based on the biological information and assigning separate priors to molecular markers in different classes. It has been shown that such methods can achieve higher prediction accuracy than conventional methods in some circumstances. However, these methods mainly focus on single-trait analysis, and available priors of these methods are limited. Thus, in both single-trait and multiple-trait analysis, we propose the multi-class Bayesian Alphabet methods, in which multiple Bayesian Alphabet priors, including RR-BLUP, BayesA, BayesB, BayesCΠ, and Bayesian LASSO, can be used for markers allocated to different classes. The superior performance of the multi-class Bayesian Alphabet in genomic prediction is demonstrated using both real and simulated data. The software tool JWAS offers open-source routines to perform these analyses.

scholarly journals Application of Multi-SNP Approaches Bayesian LASSO and AUC-RF to Detect Main Effects of Inflammatory-Gene Variants Associated with Bladder Cancer Risk

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e83745 ◽  
Author(s):  
Evangelina López de Maturana ◽  
Yuanqing Ye ◽  
M. Luz Calle ◽  
Nathaniel Rothman ◽  
Víctor Urrea ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Cancer Risk ◽  
Gene Variants ◽  
Bladder Cancer Risk ◽  
Bayesian Lasso ◽  
Inflammatory Gene ◽  
Main Effects
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