IPF-LASSO: Integrative L (1)-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data

As modern biotechnologies advance, it has become increasingly frequent that different modalities of high-dimensional molecular data (termed “omics” data in this paper), such as gene expression, methylation, and copy number, are collected from the same patient cohort to predict the clinical outcome....

Descripción completa

Detalles Bibliográficos
Autores principales: Boulesteix, Anne-Laure, De Bin, Riccardo, Jiang, Xiaoyu, Fuchs, Mathias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5435977/
https://www.ncbi.nlm.nih.gov/pubmed/28546826
http://dx.doi.org/10.1155/2017/7691937

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5435977/
https://www.ncbi.nlm.nih.gov/pubmed/28546826
http://dx.doi.org/10.1155/2017/7691937

Ejemplares similares