Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction

  1. Xiaodi Yang
  2. Shiping Yang
  3. Xianyi Lian
  4. Stefan Wuchty
  5. Ziding Zhang

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Abstract

To predict interactions between human and viral proteins, we combine evolutionary sequence profile features with a Siamese convolutional neural network (CNN) architecture and a multi-layer perceptron (MLP). Our architecture outperforms various feature encodings-based machine learning and state-of-the-art prediction methods. As our main contribution, we introduce two types of transfer learning methods (i.e., ‘frozen’ type and ‘fine-tuning’ type) that reliably predict interactions in a target human-virus domain based on training in a source human-virus domain, by retraining CNN layers. Our transfer learning strategies can effectively apply prior knowledge transfer from large source dataset/task to small target dataset/task to improve prediction performance. Finally, we utilize the ‘frozen’ type of transfer learning to predict human-SARS-CoV-2 PPIs, indicating that our predictions are topologically and functionally similar to experimentally known interactions. Source code and datasets are available at https://github.com/XiaodiYangCAU/TransPPI/ .

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  1. ScreenIT

    SciScore for 10.1101/2021.02.16.431420: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Enrichment analysis: Module identification and functional analysis of the modules: In order to build the integrated interaction network for topological analysis, we first collected known protein interactions between the human proteins predicted to interact with SARS-CoV-2 from the HIPPIE database (Alanis-Lobato et al., 2017).
    HIPPIE
    suggested: (HIPPIE, RRID:SCR_014651)
    Visualizations of the modules (i.e., subnetworks) were carried out with Cytoscape (Shannon et al., 2003).
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    Enrichment analysis for each cluster was performed by using hypergeometric tests, where corresponding P-values were Bonferroni corrected, and only the five most enriched GO BP terms and KEGG pathways were considered (Adjusted P-value ≤ 0.05) in Figure 6.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2021.02.16.431420 on bioRxiv