ArticleBioinformatics (Oxford, England)2010
Identification of non-Hodgkin's lymphoma prognosis signatures using the CTGDR method.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Oral non-Hodgkin's lymphoma in a patient with rheumatoid arthritis treated with etanercept and methotrexate.Journal of clinical and experimental dentistry · 2015Article
- Integrative analysis of prognosis data on multiple cancer subtypes.Biometrics · 2014Article
- Bayesian hierarchical structured variable selection methods with application to MIP studies in breast cancer.Journal of the Royal Statistical Society. Series C, Applied statistics · 2014Article
- Bayesian joint selection of genes and pathways: applications in multiple myeloma genomics.Cancer informatics · 2014Article
- Network-guided sparse regression modeling for detection of gene-by-gene interactions.Bioinformatics (Oxford, England) · 2013Article
- Integrative analysis of cancer prognosis data with multiple subtypes using regularized gradient descent.Genetic epidemiology · 2012Article
- Risk Factors of Follicular Lymphoma.Expert opinion on medical diagnostics · 2012Article
- Risk Factors of Non-Hodgkin Lymphoma.Expert opinion on medical diagnostics · 2011Article
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9 authors.
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Abstract
motivationAlthough NHL (non-Hodgkin's lymphoma) is the fifth leading cause of cancer incidence and mortality in the USA, it remains poorly understood and is largely incurable. Biomedical studies have shown that genomic variations, measured with SNPs (single nucleotide polymorphisms) in genes, may have independent predictive power for disease-free survival in NHL patients beyond clinical measurements.
resultsWe apply the CTGDR (clustering threshold gradient directed regularization) method to genetic association studies using SNPs, analyze data from an association study of NHL and identify prognosis signatures to diffuse large B cell lymphoma (DLBCL) and follicular lymphoma (FL), the two most common subtypes of NHL. With the CTGDR method, we are able to account for the joint effects of multiple genes/SNPs, whereas most existing studies are single-marker based. In addition, we are able to account for the 'gene and SNP-within-gene' hierarchical structure and identify not only predictive genes but also predictive SNPs within identified genes. In contrast, existing studies are limited to either gene or SNP identification, but not both. We propose using resampling methods to evaluate the predictive power and reproducibility of identified genes and SNPs. Simulation study and data analysis suggest satisfactory performance of the CTGDR method.
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