GNE: a deep learning framework for gene network inference by aggregating biological information.

Journal: BMC systems biology
Published Date: Apr 5, 2019

Abstract

BACKGROUND: The topological landscape of gene interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, it is still a challenging task to aggregate heterogeneous biological information such as gene expression and gene interactions to achieve more accurate inference for prediction and discovery of new gene interactions. In particular, how to generate a unified vector representation to integrate diverse input data is a key challenge addressed here.

Authors

  • Kishan Kc
    Golisano College of Computing and Information Sciences, Rochester Institute of Technology, 20 Lomb Memorial Drive, Rochester, New York, 14623, USA. kk3671@rit.edu.
  • Rui Li
    Department of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China.
  • Feng Cui
    Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, 84 Lomb Memorial Drive, Rochester, New York, 14623, USA.
  • Qi Yu
    Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Anne R Haake
    Golisano College of Computing and Information Sciences, Rochester Institute of Technology, 20 Lomb Memorial Drive, Rochester, New York, 14623, USA.

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