| P2093 | author name string | Johann Sölkner | |
| | Patrik Waldmann | |
| | Gábor Mészáros | |
| | Christian Fuerst | |
| | Birgit Gredler | |
| P2860 | cites work | Brief review of regression-based and machine learning methods in genetic epidemiology: the Genetic Analysis Workshop 17 experience. | Q35937680 |
| | Genome-wide associations for milk production and somatic cell score in Holstein-Friesian cattle in Ireland | Q35991778 |
| | Whole-genome regression and prediction methods applied to plant and animal breeding | Q36596450 |
| | Priors in whole-genome regression: the bayesian alphabet returns | Q36972191 |
| | Methods for handling multiple testing | Q37115915 |
| | Genome-wide association analysis by lasso penalized logistic regression | Q37319644 |
| | New approaches to population stratification in genome-wide association studies | Q37765137 |
| | A Selective Overview of Variable Selection in High Dimensional Feature Space. | Q37875701 |
| | Accommodating linkage disequilibrium in genetic-association analyses via ridge regression | Q39142014 |
| | A SPECTRAL GRAPH APPROACH TO DISCOVERING GENETIC ANCESTRY. | Q42082036 |
| | SNP Selection in genome‐wide and candidate gene studies via penalized logistic regression | Q42259366 |
| | Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition | Q43876933 |
| | Joint identification of multiple genetic variants via elastic-net variable selection in a genome-wide association analysis | Q43952672 |
| | Machine learning in genome-wide association studies. | Q45963331 |
| | LASSO with cross-validation for genomic selection. | Q51724996 |
| | Population structure and eigenanalysis | Q21145248 |
| | Genetic architecture of complex traits and accuracy of genomic prediction: coat colour, milk-fat percentage, and type in Holstein cattle as contrasting model traits | Q21563392 |
| | Genome-wide association studies for complex traits: consensus, uncertainty and challenges | Q24550632 |
| | Regularization Paths for Generalized Linear Models via Coordinate Descent | Q29614491 |
| | BioVenn - a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams | Q33377261 |
| | Comparison between single-marker analysis using Merlin and multi-marker analysis using LASSO for Framingham simulated data | Q33519284 |
| | Prioritizing GWAS results: A review of statistical methods and recommendations for their application | Q33571786 |
| | Overview of techniques to account for confounding due to population stratification and cryptic relatedness in genomic data association analyses. | Q33632115 |
| | Deregressing estimated breeding values and weighting information for genomic regression analyses | Q33634617 |
| | Bioinformatics challenges for genome-wide association studies | Q33647983 |
| | Multi-population GWA mapping via multi-task regularized regression | Q33896238 |
| | Comparison of analyses of the QTLMAS XIV common dataset. II: QTL analysis | Q33915780 |
| | QTLMAS 2010: simulated dataset | Q33915788 |
| | Optimized application of penalized regression methods to diverse genomic data | Q34096074 |
| | Genomic position mapping discrepancies of commercial SNP chips | Q34170462 |
| | Risk prediction using genome‐wide association studies | Q34242423 |
| | Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions | Q34284461 |
| | Mapping genes for complex traits in domestic animals and their use in breeding programmes | Q34981451 |
| P2507 | corrigendum / erratum | Corrigendum: Evaluation of the lasso and the elastic net in genome-wide association studies. | Q46816938 |
| P921 | main subject | genome-wide association study | Q1098876 |
| P304 | page(s) | 270 | |
| P577 | publication date | 2013-12-04 | |
| P1433 | published in | Frontiers in Genetics | Q2499875 |
| P1476 | title | Evaluation of the lasso and the elastic net in genome-wide association studies | |
| P478 | volume | 4 | |