Next Article in Journal
The Origin of a Coastal Indigenous Horse Breed in China Revealed by Genome-Wide SNP Data
Previous Article in Journal
A Novel Insight into Functional Divergence of the MST Gene Family in Rice Based on Comprehensive Expression Patterns
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Genes 2019, 10(3), 240;

Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features

Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
Mihaylo College of Business and Economics, California State University Fullerton, Fullerton, CA 92831, USA
Division of Periodontology, Diagnostic Sciences and Dental Hygiene, and Division of Biomedical Sciences, Herman Ostrow School of Dentistry, University of Southern California, Los Angeles, CA 90089, USA
Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 19 February 2019 / Revised: 12 March 2019 / Accepted: 18 March 2019 / Published: 21 March 2019
(This article belongs to the Special Issue Computational Oncogenomics)
PDF [2101 KB, uploaded 21 March 2019]


Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for instance, to predict the survival of patients censored at specific time points. Multi-omics data, including genome-wide gene expression, methylation, protein expression, copy number alteration, and somatic mutation data, are becoming increasingly common in cancer studies. To harness the rich information in multi-omics data, we developed GDP (Group lass regularized Deep learning for cancer Prognosis), a computational tool for survival prediction using both clinical and multi-omics data. GDP integrated a deep learning framework and Cox proportional hazard model (CPH) together, and applied group lasso regularization to incorporate gene-level group prior knowledge into the model training process. We evaluated its performance in both simulated and real data from The Cancer Genome Atlas (TCGA) project. In simulated data, our results supported the importance of group prior information in the regularization of the model. Compared to the standard lasso regularization, we showed that group lasso achieved higher prediction accuracy when the group prior knowledge was provided. We also found that GDP performed better than CPH for complex survival data. Furthermore, analysis on real data demonstrated that GDP performed favorably against other methods in several cancers with large-scale omics data sets, such as glioblastoma multiforme, kidney renal clear cell carcinoma, and bladder urothelial carcinoma. In summary, we demonstrated that GDP is a powerful tool for prognosis of patients with cancer, especially when large-scale molecular features are available. View Full-Text
Keywords: deep learning; genomics; cancer; survival analysis deep learning; genomics; cancer; survival analysis

Figure 1

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 (CC BY 4.0).

Supplementary material


Share & Cite This Article

MDPI and ACS Style

Xie, G.; Dong, C.; Kong, Y.; Zhong, J.F.; Li, M.; Wang, K. Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features. Genes 2019, 10, 240.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Genes EISSN 2073-4425 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top