Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier.

Journal: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Published Date: Jan 1, 2019

Abstract

BACKGROUND: MicroRNAs (miRNAs) are small, non-coding RNA that regulate gene expression through post-transcriptional silencing. Differential expression observed in miRNAs, combined with advancements in deep learning (DL), have the potential to improve cancer classification by modelling non-linear miRNA-phenotype associations. We propose a novel miRNA-based deep cancer classifier (DCC) incorporating genomic and hierarchical tissue annotation, capable of accurately predicting the presence of cancer in wide range of human tissues.

Authors

  • Blake Pyman
    School of Computing, Queen's University, Kingston, Ontario K7L 3N6, Canada http://www.queensu.ca/, pyman@cs.queensu.ca.
  • Alireza Sedghi
  • Shekoofeh Azizi
  • Kathrin Tyryshkin
    Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, 88 Stuart St, Kingston, ON K7L 3N6, Canada; School of Computing, Queen's University, 557 Goodwin Hall, Kingston, ON K7L 2N8, Canada. Electronic address: kt40@queensu.ca.
  • Neil Renwick
    Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, 88 Stuart St, Kingston, ON K7L 3N6, Canada.
  • Parvin Mousavi
    Medical Informatics Laboratory, School of Computing, Queen's University, 557 Goodwin Hall, Kingston, ON K7L 2N8, Canada.

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