ArticleBioinformatics (Oxford, England)2019
Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers.
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105 citing papers in PubMed.
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- Machine learning identifies inflammation-related diagnostic biomarkers for primary myelofibrosis with clinical validation.Scientific reports · 2025Article
- The diagnostic value of urinary exosomes in prostate cancer: a prospective multicenter study.BMC urology · 2025Article
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- Frequentist Grouped Weighted Quantile Sum Regression for Correlated Chemical Mixtures.Statistics in medicine · 2025Article
- Machine learning and metabolomics identify biomarkers associated with the disease extent of ulcerative colitis.Journal of Crohn's & colitis · 2025Article
- An integrative analysis reveals cancer risk associated with artificial sweeteners.Journal of translational medicine · 2025Article
- YY2 Serves as a Novel Prognostic Biomarker Correlated with Immune Microenvironment and Glycolysis in Esophageal Carcinoma.Current genomics · 2025Article
- CRISPR/Cas9-based discovery of ccRCC therapeutic opportunities through molecular mechanism and immune microenvironment analysis.Frontiers in immunology · 2025Article
45 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
motivationAssociation studies to discover links between genetic markers and phenotypes are central to bioinformatics. Methods of regularized regression, such as variants of the Lasso, are popular for this task. Despite the good predictive performance of these methods in the average case, they suffer from unstable selections of correlated variables and inconsistent selections of linearly dependent variables. Unfortunately, as we demonstrate empirically, such problematic situations of correlated and linearly dependent variables often exist in genomic datasets and lead to under-performance of classical methods of variable selection.
resultsTo address these challenges, we propose the Precision Lasso. Precision Lasso is a Lasso variant that promotes sparse variable selection by regularization governed by the covariance and inverse covariance matrices of explanatory variables. We illustrate its capacity for stable and consistent variable selection in simulated data with highly correlated and linearly dependent variables. We then demonstrate the effectiveness of the Precision Lasso to select meaningful variables from transcriptomic profiles of breast cancer patients. Our results indicate that in settings with correlated and linearly dependent variables, the Precision Lasso outperforms popular methods of variable selection such as the Lasso, the Elastic Net and Minimax Concave Penalty (MCP) regression. AVAILABILITY AND IMPLEMENTATION: Software is available at https://github.com/HaohanWang/thePrecisionLasso. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.