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Towards A More Effective Bidirectional LSTM-Based Learning Model for Human-Bacterium Protein-Protein Interactions

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Practical Applications of Computational Biology & Bioinformatics, 14th International Conference (PACBB 2020) (PACBB 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1240))

Abstract

The identification of protein-protein interaction (PPI) is one of the most important tasks to understand the biological functions and disease mechanisms. Although numerous databases of biological interactions have been published in debt to advanced high-throughput technology, the study of inter-species protein-protein interactions, especially between human and bacterium pathogens, remains an active yet challenging topic to harness computational models tackling the complex analysis and prediction tasks. In this paper, we comprehensively revisit the prediction task of human-bacterium protein-protein interactions (HB-PPI), which is a first ever endeavour to report an empirical evaluation in learning and predicting HB-PPI based on machine learning models. Firstly, we summarise the literature review of human-bacterium interaction (HBI) study, meanwhile a vast number of databases published in the last decades are carefully examined. Secondly, a broader and deeper experimental framework is designed for HB-PPI prediction task, which explores a variety of feature representation algorithms and different computational models to learn from the curated HB-PPI dataset and perform predictions. Furthermore, a bidirectional LSTM-based model is proposed for the prediction task, which demonstrates a more effective performance in comparison with the others. Finally, opportunities for improving the performance and robustness of machine learning models for HP-PPI prediction are also discussed, laying a foundation for future work.

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Notes

  1. 1.

    The code and data are available on: https://huaming-chen.com/Bi-LSTM-Predictor/.

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Author information

Authors and Affiliations

  1. University of Wollongong, Wollongong, NSW, 2500, Australia

    Huaming Chen, Jun Shen & Lei Wang

  2. University of Surrey, Guildford, GU2 7XH, UK

    Yaochu Jin

Authors
  1. Huaming Chen
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  2. Jun Shen
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  3. Lei Wang
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  4. Yaochu Jin
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Corresponding author

Correspondence to Huaming Chen .

Editor information

Editors and Affiliations

  1. Enhanced Regenerative Medicine, Istituto Italiano di Tecnologia, Genoa, Genova, Italy

    Gabriella Panuccio

  2. Department de Informática, Universidade do Minho, Braga, Portugal

    Miguel Rocha

  3. Computer Science Department, University of Vigo, Vigo, Spain

    Florentino Fdez-Riverola

  4. Institute for Artificial Intelligence and Big Data (AIBIG), Universiti Malaysia Kelantan, Kampus Kota, Kota Bharu, Malaysia

    Mohd Saberi Mohamad

  5. Biotechnology, Intelligent Systems and Educational Technology (BISITE) Research Group, University of Salamanca, Salamanca, Salamanca, Spain

    Roberto Casado-Vara

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Chen, H., Shen, J., Wang, L., Jin, Y. (2021). Towards A More Effective Bidirectional LSTM-Based Learning Model for Human-Bacterium Protein-Protein Interactions. In: Panuccio, G., Rocha, M., Fdez-Riverola, F., Mohamad, M., Casado-Vara, R. (eds) Practical Applications of Computational Biology & Bioinformatics, 14th International Conference (PACBB 2020). PACBB 2020. Advances in Intelligent Systems and Computing, vol 1240. Springer, Cham. https://doi.org/10.1007/978-3-030-54568-0_10

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