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Variable selection in omics data: A practical evaluation of small sample sizes
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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The use of random-effect models for high-dimensional variable selection problems
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Evaluation of variable selection methods for random forests and omics data sets
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Is cross-validation valid for small-sample microarray classification?
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MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data
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Permutation importance: a corrected feature importance measure
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Development of a Metabolic Biosignature for Detection of Early Lyme Disease
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Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools
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Disease-specific biases in alternative splicing and tissue-specific dysregulation revealed by multitissue profiling of lymphocyte gene expression in type 1 diabetes
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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Regularization and Variable Selection Via the Elastic Net
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THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
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Regression Shrinkage and Selection Via the Lasso
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Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring
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Exploring Privacy Preservation in Outsourced K-Nearest Neighbors with Multiple Data Owners
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Classification and Selection of Biomarkers in Genomic Data Using LASSO
inferredcites →https://doi.org/10.1186/1471-2105-12-253openalex-snapshot
Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems
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Elastic-net regularization approaches for genome-wide association studies of rheumatoid arthritis
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Variable selection methods for developing a biomarker panel for prediction of dengue hemorrhagic fever
inferredcites →https://doi.org/10.1186/s12864-015-2353-zopenalex-snapshot
Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster
inferredcites →https://doi.org/10.1186/s12874-015-0066-2openalex-snapshot
Comparison of subset selection methods in linear regression in the context of health-related quality of life and substance abuse in Russia
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Least absolute shrinkage and selection operator type methods for the identification of serum biomarkers of overweight and obesity: simulation and application
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r2VIM: A new variable selection method for random forests in genome-wide association studies
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High-dimensional classification using features annealed independence rules
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Challenges in biomarker discovery: combining expert insights with statistical analysis of complex omics data
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A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing
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Regularization Paths for Generalized Linear Models via Coordinate Descent
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Feature Selection with the Boruta Package
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Statistical Power Analysis for the Behavioral Sciences
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Analytical Methods in Untargeted Metabolomics: State of the Art in 2015
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Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data
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PLS-Based and Regularization-Based Methods for the Selection of Relevant Variables in Non-targeted Metabolomics Data
inferredcites →https://doi.org/10.3832/ifor1495-008openalex-snapshot
Chemometric technique performances in predicting forest soil chemical and biological properties from UV-Vis-NIR reflectance spectra with small, high dimensional datasets
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Overcome Support Vector Machine Diagnosis Overfitting
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The Importance of Experimental Design And Qc Samples in Large-Scale And Ms-Driven Untargeted Metabolomic Studies of Humans
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Comparison of Regularized Regression Methods for ~Omics Data
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Comparison of 14 different families of classification algorithms on 115 binary datasets
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Additional file 1 of Enhancing data pipelines for forecasting student performance: integrating feature selection with cross-validation
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Additional file 1 of Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients
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Additional file 2 of Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients
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