A data-driven approach to build a predictive model of cancer patients' disease outcome by utilizing co-expression networks.

Journal: Computers in biology and medicine
Published Date: Aug 15, 2020

Abstract

BACKGROUND: Next Generation Sequencing (NGS) technologies have revolutionized genomics data research over the last decades by facilitating high-throughput sequencing of genetic material such as RNA Sequencing (RNAseq). A significant challenge is to explore innovative methods for further exploitation of these large-scale datasets. The approach described in this paper utilizes the results of RNAseq analysis to identify biomarkers related to the disease and deploy a disease outcome predictive model.

Authors

  • A Kosvyra
    Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece. Electronic address: aekosvyra@auth.gr.
  • C Maramis
    Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
  • I Chouvarda
    Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.

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