Protein Family Classification with Multi-Layer Graph Convolutional Networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Next-generation sequencing techniques provide us with an opportunity for generating sequenced proteins and identifying the biological families and functions of the proteins. However, compared with identified proteins, uncharacterized proteins consist of a notable percentage of the predicted proteins in bioinformatics research field. However, previous clustering based algorithms heavily relying on large data samples are not accurate enough to assign protein families given a small amount of family annotated proteins. Therefore, considering limited protein data with annotated protein families, a more accurate and faster protein family prediction method is required. In this paper, we apply the Multi-layer Graph Convolutional Networks (GCN) architecture on a Protein-Protein Interaction (PPI) network with limited characterized proteins to explore the performance of protein family classification by taking into account both of the network topology and physicochemical protein amino acid features.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2390-2393
Number of pages4
ISBN (Electronic)9781538654880
DOIs
StatePublished - Jan 21 2019
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: Dec 3 2018Dec 6 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
CountrySpain
CityMadrid
Period12/3/1812/6/18

Keywords

  • Graph Convolutional Networks
  • Protein Family Classification
  • Protein-Protein Interaction Network

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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  • Cite this

    Zhang, D., & Kabuka, M. R. (2019). Protein Family Classification with Multi-Layer Graph Convolutional Networks. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 2390-2393). [8621520] (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621520