Exploring Pathway-Based Group Lasso for Cancer Survival Analysis: A Special Case of Multi-Task Learning
Motivation: The Cox proportional hazard models are widely used in the study of cancer survival. However, these models often meet challenges such as the large number of features and small sample sizes of cancer data sets. While this issue can be partially solved by applying regularization techniques...
Guardado en:
Autores principales: | Gabriela Malenová, Daniel Rowson, Valentina Boeva |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/b971f490aafb41a4b477f53f7a465abd |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Lasso Proteins—Unifying Cysteine Knots and Miniproteins
por: Bartosz Ambroży Greń, et al.
Publicado: (2021) -
LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer
por: Shao-Hua Yu, et al.
Publicado: (2021) -
Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence
por: Farzaneh Hamidi, et al.
Publicado: (2021) -
Pruning Filters Base on Extending Filter Group Lasso
por: Zhihong Xie, et al.
Publicado: (2020) -
New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection
por: Émeline Courtois, et al.
Publicado: (2021)