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 contribution

1 Citation (Scopus)

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.

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
PublisherSpringer
Pages550-561
Number of pages12
ISBN (Electronic)978-3-030-45385-5
ISBN (Print)978-3-030-45384-8
DOIs
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)
PublisherSpringer
Volume12108 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2020
CountrySpain
CityGranada
Period6/05/208/05/20

Keywords

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

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