ArticleAdvances and applications in bioinformatics and chemistry : AABC2008
Identification of significant genes in genomics using Bayesian variable selection methods.
Article in Advances and applications in bioinformatics and chemistry : AABC, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A novel mutation in Slc2a4 as a mouse model of fatigue.Genes, brain, and behavior · 2019Article
- Machine learning and systems genomics approaches for multi-omics data.Biomarker research · 2017Review
- Bayesian Variable Selection under the Proportional Hazards Mixed-effects Model.Computational statistics & data analysis · 2014Article
- Comparison of classification algorithms with wrapper-based feature selection for predicting osteoporosis outcome based on genetic factors in a taiwanese women population.International journal of endocrinology · 2013Article
- Pilot study of an association between a common variant in the non-muscle myosin heavy chain 9 (MYH9) gene and type 2 diabetic nephropathy in a Taiwanese population.The application of clinical genetics · 2010Article
- A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data.Journal of translational medicine · 2009Article
- A support vector machine approach to assess drug efficacy of interferon-alpha and ribavirin combination therapy.Molecular diagnosis & therapy · 2008Article
- Association study of a brain-derived neurotrophic-factor polymorphism and short-term antidepressant response in major depressive disorders.Pharmacogenomics and personalized medicine · 2008Article
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Authors and funding
2 authors.
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Abstract
In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for research ranging from candidate gene studies to genome-wide association studies. In this study, we proposed a Bayesian method for identifying the promising candidate genes that are significantly more influential than the others. We employed the framework of variable selection and a Gibbs sampling based technique to identify significant genes. The proposed approach was applied to a genomics study for persons with chronic fatigue syndrome. Our studies show that the proposed Bayesian methodology is effective for deriving models for genomic studies and for providing information on significant genes.
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