GCRNN: graph convolutional recurrent neural network for compound-protein interaction prediction.

Journal: BMC bioinformatics
Published Date: Jan 11, 2022

Abstract

BACKGROUND: Compound-protein interaction prediction is necessary to investigate health regulatory functions and promotes drug discovery. Machine learning is becoming increasingly important in bioinformatics for applications such as analyzing protein-related data to achieve successful solutions. Modeling the properties and functions of proteins is important but challenging, especially when dealing with predictions of the sequence type.

Authors

  • Ermal Elbasani
    Department of Computer Science and Engineering, Sun Moon University, Asan 31460, Republic of Korea.
  • Soualihou Ngnamsie Njimbouom
    Department of Computer Science and Engineering, Sun Moon University, Asan, 31460, South Korea.
  • Tae-Jin Oh
    Genome-Based BioIT Convergence Institute, Sun Moon University, Asan, 31460, South Korea.
  • Eung-Hee Kim
    Biomedical Knowledge Engineering Laboratory, Seoul National University, Seoul, Republic of Korea.
  • Hyun Lee
    Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea.
  • Jeong-Dong Kim
    Department of Computer Science and Engineering, Sun Moon University, Asan 31460, Republic of Korea.

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