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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<jats:monospace>ipflasso<\/jats:monospace>, 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.<\/jats:p>","DOI":"10.1155\/2017\/7691937","type":"journal-article","created":{"date-parts":[[2017,5,9]],"date-time":"2017-05-09T13:26:59Z","timestamp":1494336419000},"page":"1-14","source":"Crossref","is-referenced-by-count":79,"title":["IPF-LASSO: Integrative<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mrow><mml:msub><mml:mrow><mml:mi>L<\/mml:mi><\/mml:mrow><mml:mrow><mml:mn fontstyle=\"italic\">1<\/mml:mn><\/mml:mrow><\/mml:msub><\/mml:mrow><\/mml:math>-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data"],"prefix":"10.1155","volume":"2017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2729-0947","authenticated-orcid":true,"given":"Anne-Laure","family":"Boulesteix","sequence":"first","affiliation":[{"name":"Department of Medical Informatics, Biometry and Epidemiology, University of Munich (LMU), Marchioninistr. 15, 81377 Munich, 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