Abstract
Cancer is a complex disease with significant social and economic impact. Advancements in high-throughput molecular assays and the reduced cost for performing high-quality multi-omic measurements have fuelled insights through machine learning. Previous studies have shown promise on using multiple omic layers to predict survival and stratify cancer patients. In this paper, we develop and report a Supervised Autoencoder (SAE) model for survival-based multi-omic integration, which improves upon previous work, as well as a Concrete Supervised Autoencoder model (CSAE) which uses feature selection to jointly reconstruct the input features as well as to predict survival. Our results show that our models either outperform or are on par with some of the most commonly used baselines, while either providing a better survival separation (SAE) or being more interpretable (CSAE). Feature selection stability analysis on our models shows a power-law relationship with features commonly associated with survival. The code for this project is available at: https://github.com/phcavelar/coxae.
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Acknowledgements
We would like to thank Dr Jonathan Cardoso-Silva for fruitful conversations, and João Nuno Beleza Oliveira Vidal Lourenço for designing the diagrams. P.H.C.A. acknowledges that during his stay at KCL and A*STAR he’s partly funded by King’s College London and the A*STAR Research Attachment Programme (ARAP). The research was also supported by the National Institute for Health Research Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London (IS-BRC-1215-20006). The authors are solely responsible for study design, data collection, analysis, decision to publish, and preparation of the manuscript. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. This work used King’s CREATE compute cluster for its experiments [18]. The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
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da Costa Avelar, P.H., Laddach, R., Karagiannis, S.N., Wu, M., Tsoka, S. (2023). Multi-omic Data Integration and Feature Selection for Survival-Based Patient Stratification via Supervised Concrete Autoencoders. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13811. Springer, Cham. https://doi.org/10.1007/978-3-031-25891-6_5
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