Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning-based neural network.

Journal: GigaScience
Published Date: Jul 1, 2020

Abstract

BACKGROUND: Gene expression plays a key intermediate role in linking molecular features at the DNA level and phenotype. However, owing to various limitations in experiments, the RNA-seq data are missing in many samples while there exist high-quality of DNA methylation data. Because DNA methylation is an important epigenetic modification to regulate gene expression, it can be used to predict RNA-seq data. For this purpose, many methods have been developed. A common limitation of these methods is that they mainly focus on a single cancer dataset and do not fully utilize information from large pan-cancer datasets.

Authors

  • Xiang Zhou
    Department of Sociology, Harvard University, Cambridge, Massachusetts, USA.
  • Hua Chai
    Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Avenida Wai Long,Taipa, Macau, 999078, China.
  • Huiying Zhao
    Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 Yan Jiang West Road, Guangzhou 510120, China.
  • Ching-Hsing Luo
    Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan; Institute of Medical Science and Technology, National Sun Yat-sen University, KaoHsiung 80424, Taiwan. Electronic address: robinluo@mail.ncku.edu.tw.
  • Yuedong Yang
    Institute for Glycomics and School of Information and Communication Technique, Griffith University, Parklands Dr. Southport, QLD 4222, Australia.

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