Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches

DOI10.1515/sagmb-2015-0072zbMath1344.92016OpenAlexW2400517226WikidataQ31102072 ScholiaQ31102072MaRDI QIDQ309421

Jana L. Gevertz, Chamont W. H. Wang

Publication date: 7 September 2016

Published in: Statistical Applications in Genetics and Molecular Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/sagmb-2015-0072

zbMATH Keywords

classificationvariable selectionfalse discovery rategene identificationshrinkage and regularization techniques

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) General biostatistics (92B15)

Uses Software

• RankGene
• penalizedSVM
• GWAsimulator
• care
• lemma

Cites Work

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• Greedy function approximation: A gradient boosting machine.
• High-Dimensional Regression and Variable Selection Using CAR Scores
• The Adaptive Lasso and Its Oracle Properties
• Latent class analysis variable selection
• Recent advances in predictive (machine) learning
• On the Benjamini-Hochberg method
• Mining supervised classification performance studies: a meta-analytic investigation
• Microarrays, empirical Bayes and the two-groups model
• The future of indirect evidence
• Laplace approximated EM microarray analysis: an empirical Bayes approach for comparative microarray experiments
• A decision-theoretic generalization of on-line learning and an application to boosting
• Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data
• Multiple hypothesis testing in microarray experiments.
• Learning kernel logistic regression in the presence of class label noise
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Least angle regression. (With discussion)
• Boosting a weak learning algorithm by majority
• Classifier technology and the illusion of progress
• Breast Cancer Diagnosis from Proteomic Mass Spectrometry Data: A Comparative Evaluation
• The Joint Null Criterion for Multiple Hypothesis Tests
• False discovery rates and copy number variation
• Machine Learning Approaches to Bioinformatics
• Penalized logistic regression for detecting gene interactions
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Regression Shrinkage and Selection via The Lasso: A Retrospective
• Strong Control, Conservative Point Estimation and Simultaneous Conservative Consistency of False Discovery Rates: A Unified Approach
• A Direct Approach to False Discovery Rates
• 10.1162/153244303322753616
• 10.1162/153244303322753751
• Assessing the Performance of Classification Methods
• On the Non-Negative Garrotte Estimator
• A Bayesian Mixture Model for Differential Gene Expression
• Gene selection for cancer classification using support vector machines
• A selective review of group selection in high-dimensional models