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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 7691937, 14 pages
https://doi.org/10.1155/2017/7691937
Research Article

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

1Department of Medical Informatics, Biometry and Epidemiology, University of Munich (LMU), Marchioninistr. 15, 81377 Munich, Germany
2Department of Mathematics, University of Oslo, Moltke Moes Vei 3, 0851 Oslo, Norway
3Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139, USA
4Biogen, 225 Binney Street, Cambridge, MA 02142, USA

Correspondence should be addressed to Anne-Laure Boulesteix; ed.nehcneum-inu.dem.ebi@xietseluob

Received 20 January 2017; Accepted 14 March 2017; Published 4 May 2017

Academic Editor: Andreas Mayr

Copyright © 2017 Anne-Laure Boulesteix et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.