Abstract
The accessibility to “big data” sets down an ambitious challenge in the medical field, especially in personalized medicine, where gene expression data are increasingly being used to establish a diagnosis and optimize treatment of oncological patients. However, the high-dimensionality nature of the data brings many constraints, for which several approaches have been considered, with regularization techniques in the cutting-edge research front. Additionally, the network structure of gene expression data has fostered the development of network-based regularization techniques to convey data into a low-dimensional and interpretable level. In this work, classical elastic net and two recently proposed network-based methods, HubCox and OrphanCox, are applied to high-dimensional gene expression data, to model survival data. An oncological transcriptomic dataset obtained from The Cancer Genome Atlas (TCGA) is used, with patients’ RNA-seq measurements as covariates. The application of sparsity-inducing techniques to the dataset enabled the selection of relevant genes over a range of parameters evaluated. Comparable results were obtained for the elastic net and the network-based OrphanCox regarding model performance and genes selected.
Partially funded by H2020 (No. 633974) and the Portuguese Foundation for Science & Technology FCT (UIDB/00297/2020, UIDB/04516/2020, UIDB/50021/2020, UIDB/50022/2020, PTDC/CCI-CIF/29877/2017, PTDC/CCI-INF/29168/2017 and SFRH/BD/97415/2013).
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Carrasquinha, E., Veríssimo, A., Lopes, M.B., Vinga, S. (2020). Network-Based Variable Selection for Survival Outcomes in Oncological Data. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_49
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