Boosting and lassoing new prostate cancer SNP risk factors and their connection to selenium
Abstract We begin by arguing that the often used algorithm for the discovery and use of disease risk factors, stepwise logistic regression, is unstable. We then argue that there are other algorithms available that are much more stable and reliable (e.g. the lasso and gradient boosting). We then prop...
Guardado en:
Autores principales: | David E. Booth, Venugopal Gopalakrishna-Remani, Matthew L. Cooper, Fiona R. Green, Margaret P. Rayman |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/7ba5a9efd9e749778f882d2f6860e03c |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
KLK3 SNP–SNP interactions for prediction of prostate cancer aggressiveness
por: Hui-Yi Lin, et al.
Publicado: (2021) -
Functional Connectivity During Visuospatial Processing in Schizophrenia: A Classification Study Using Lasso Regression
por: Potvin S, et al.
Publicado: (2021) -
Lasso Proteins—Unifying Cysteine Knots and Miniproteins
por: Bartosz Ambroży Greń, et al.
Publicado: (2021) -
Polymorphisms in thioredoxin reductase and selenoprotein K genes and selenium status modulate risk of prostate cancer.
por: Catherine Méplan, et al.
Publicado: (2012) -
Pruning Filters Base on Extending Filter Group Lasso
por: Zhihong Xie, et al.
Publicado: (2020)