IPF-LASSO: Integrative L1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data
Joint Authors
Boulesteix, Anne-Laure
De Bin, Riccardo
Jiang, Xiaoyu
Fuchs, Mathias
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-05-04
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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.
While prediction based on omics data has been widely studied in the last fifteen years, little has been done in the statistical literature on the integration of multiple omics modalities to select a subset of variables for prediction, which is a critical task in personalized medicine.
In this paper, we propose a simple penalized regression method to address this problem by assigning different penalty factors to different data modalities for feature selection and prediction.
The penalty factors can be chosen in a fully data-driven fashion by cross-validation or by taking practical considerations into account.
In simulation studies, we compare the prediction performance of our approach, called IPF-LASSO (Integrative LASSO with Penalty Factors) and implemented in the R package ipflasso, with the standard LASSO and sparse group LASSO.
The use of IPF-LASSO is also illustrated through applications to two real-life cancer datasets.
All data and codes are available on the companion website to ensure reproducibility.
American Psychological Association (APA)
Boulesteix, Anne-Laure& De Bin, Riccardo& Jiang, Xiaoyu& Fuchs, Mathias. 2017. IPF-LASSO: Integrative L1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1142316
Modern Language Association (MLA)
Boulesteix, Anne-Laure…[et al.]. IPF-LASSO: Integrative L1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1142316
American Medical Association (AMA)
Boulesteix, Anne-Laure& De Bin, Riccardo& Jiang, Xiaoyu& Fuchs, Mathias. IPF-LASSO: Integrative L1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1142316
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142316