A fast procedure for calculating importance weights in bootstrap sampling
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Isolasso: a lasso regression approach to RNA-seq based transcriptome assembly
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An experimental comparison of gene selection by Lasso and Dantzig selector for cancer classification
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Feature selection method using WF-LASSO for gene expression data analysis
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Modified versions of Bayesian Information Criterion for genome-wide association studies
Computational Statistics & Data Analysis
Coordinate ascent for penalized semiparametric regression on high-dimensional panel count data
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Support Vector Machines with L1 penalty for detecting gene-gene interactions
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Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Motivation: In ordinary regression, imposition of a lasso penalty makes continuous model selection straightforward. Lasso penalized regression is particularly advantageous when the number of predictors far exceeds the number of observations. Method: The present article evaluates the performance of lasso penalized logistic regression in case–control disease gene mapping with a large number of SNPs (single nucleotide polymorphisms) predictors. The strength of the lasso penalty can be tuned to select a predetermined number of the most relevant SNPs and other predictors. For a given value of the tuning constant, the penalized likelihood is quickly maximized by cyclic coordinate ascent. Once the most potent marginal predictors are identified, their two-way and higher order interactions can also be examined by lasso penalized logistic regression. Results: This strategy is tested on both simulated and real data. Our findings on coeliac disease replicate the previous SNP results and shed light on possible interactions among the SNPs. Availability: The software discussed is available in Mendel 9.0 at the UCLA Human Genetics web site. Contact: klange@ucla.edu Supplementary information: Supplementary data are available at Bioinformatics online.