Identification of Common Gene Signatures in Microarray and RNA-Sequencing Data Using Network-Based Regularization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Microarray and RNA-sequencing (RNA-seq) gene expression data alongside machine learning algorithms are promising in the discovery of new cancer biomarkers. However, even though they are similar in purpose, there are some fundamental differences between the two techniques. We propose a methodology for cross-platform integration, and biomarker discovery based on network-based regularization via the Twin Networks Recovery (twiner) penalty, as a strategy to enhance the selection of breast cancer gene signatures that have similar correlation patterns in both platforms. In a classification setting based on sparse logistic regression (LR) taking as classes tumor from both RNA-seq and microarray, and normal tissue samples, twiner achieved precision-recall accuracies of 99.71% and 99.57% in the training and test set, respectively. Moreover, the survival analysis results validated the biological relevance of the signatures identified by twiner. Therefore, by leveraging from the existing amount of data for microarray and RNA-seq, a single biological conclusion can be reached, independent of each technology.

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
Pages15-26
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

  • Biomarkers
  • Machine learning
  • Microarray
  • Network-based regularization
  • RNA-sequencing

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