Network-Based Variable Selection for Survival Outcomes in Oncological Data

Eunice Carrasquinha, André Veríssimo, Marta B. Lopes, Susana Vinga

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationBioinformatics and Biomedical Engineering - 8th International Work-Conference, IWBBIO 2020, Proceedings
EditorsIgnacio Rojas, Olga Valenzuela, Fernando Rojas, Luis Javier Herrera, Francisco Ortuño
Place of PublicationCham
Number of pages12
ISBN (Electronic)978-3-030-45385-5
ISBN (Print)978-3-030-45384-8
Publication statusPublished - 2020
Event8th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2020 - Granada, Spain
Duration: 6 May 20208 May 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12108 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2020


  • Gene expression data
  • High-dimensional data
  • Network-based regularization
  • Regularized optimization


Dive into the research topics of 'Network-Based Variable Selection for Survival Outcomes in Oncological Data'. Together they form a unique fingerprint.

Cite this