A deep learning framework for identifying essential proteins based on multiple biological information.

Journal: BMC bioinformatics
Published Date: Aug 4, 2022

Abstract

BACKGROUND: Essential Proteins are demonstrated to exert vital functions on cellular processes and are indispensable for the survival and reproduction of the organism. Traditional centrality methods perform poorly on complex protein-protein interaction (PPI) networks. Machine learning approaches based on high-throughput data lack the exploitation of the temporal and spatial dimensions of biological information.

Authors

  • Yi Yue
    Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, China. yyyue@ahau.edu.cn.
  • Chen Ye
    School of Computer Science and Technology & Mine Digitization Engineering Research Center of Ministry of Education of the People's Republic of China, China University of Mining and Technology, Xuzhou 221116, China.
  • Pei-Yun Peng
    Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, China.
  • Hui-Xin Zhai
    Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, China.
  • Iftikhar Ahmad
    Department of Environmental Sciences, COMSATS University Islamabad, Vehari-Campus, Vehari, 61100, Pakistan. Electronic address: iftikharahmad@ciitvehari.edu.pk.
  • Chuan Xia
    Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, China.
  • Yun-Zhi Wu
    Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, China.
  • You-Hua Zhang
    Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, China. zhangyh@ahau.edu.cn.

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