A variable selection approach for highly correlated predictors in high-dimensional genomic data

Wencan Zhu, Céline Lévy-Leduc, Nils Ternès

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DOI: 10.1093/bioinformatics/btab114
Published: 2021-02-22
Container: Bioinformatics
Publisher: Oxford University Press (OUP)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1093/bioinformatics/btab114,
  title = {A variable selection approach for highly correlated predictors in high-dimensional genomic data},
  author = {Wencan Zhu and Céline Lévy-Leduc and Nils Ternès},
  year = {2021},
  journal = {Bioinformatics},
  doi = {10.1093/bioinformatics/btab114},
  url = {https://doi.org/10.1093/bioinformatics/btab114}
}

RIS

TY  - JOUR
TI  - A variable selection approach for highly correlated predictors in high-dimensional genomic data
AU  - Wencan Zhu
AU  - Céline Lévy-Leduc
AU  - Nils Ternès
PY  - 2021
JO  - Bioinformatics
DO  - 10.1093/bioinformatics/btab114
UR  - https://doi.org/10.1093/bioinformatics/btab114
ER  -

APA

Zhu, W., Lévy-Leduc, C., & Ternès, N. (2021). A variable selection approach for highly correlated predictors in high-dimensional genomic data. Bioinformatics. https://doi.org/10.1093/bioinformatics/btab114

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crossref · retrieved 2026-11-10T06:09:22.494Z