Multimodal Deep Representation Learning for Protein-Protein Interaction Networks

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

3 Scopus citations

Abstract

Protein-protein interactions(PPIs) participate in dynamic cellular and biological processes continually in our life. Thus, it is crucial to understand the PPIs thoroughly so that we can elucidate disease occurrence, achieve the desired drug target therapeutic effect and model the protein complex structures. However, compared to the available protein sequences of different organisms, the number of revealed protein-protein interactions is relatively limited. Lots of research methods have investigated in this field to facilitate the discovery of novel PPIs. Among these methods, PPI prediction techniques that solely depend on protein sequences are more prevalent than other methods which require a wealth of domain knowledge. In this paper, a Multi-modal Deep Learning Framework is proposed by combining protein physicochemical features as well as the structural context-local features from the PPI networks. In other words, our method not only takes into account the protein sequence information but also discern the neighboring effect for protein nodes in the PPI networks. We use a stacked auto-encoder architecture together with CBOW based metapath model to examine the PPI predictions. Based on that, we use the supervised deep neural networks to both identify the PPIs and classify the protein families. The results present that our Multi-modal deep learning framework achieves better performance compared to primary methods.

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.
Pages595-602
Number of pages8
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

  • Knowledge Representation Learning
  • Multimodel Deep Neural Network
  • Protein-Protein Interaction Network

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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  • Cite this

    Zhang, D., & Kabuka, M. R. (2019). Multimodal Deep Representation Learning for Protein-Protein Interaction 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. 595-602). [8621366] (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621366