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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....
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Hindawi
2017
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| Materias: | |
| 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 |