Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings

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Abstract

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  1. SciScore for 10.1101/2022.05.31.494170: (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
    This information is not included in PPT-Ohmnet, hence, we used BioGRID and IntAct as the two largest PPI databases to extract the experimental procedures, such as “pull down”, “two hybrid”, by which the interactions were originally discovered.
    BioGRID
    suggested: (BioGrid Australia, RRID:SCR_006334)
    To train deep learning models we retrieved the sequences of all proteins in our PPIs from the UniProt database.
    UniProt
    suggested: (UniProtKB, RRID:SCR_004426)
    Collection of Human Receptor Proteins: To extract human receptor proteins, we first performed a search in GO for the term “receptor”.
    Human Receptor Proteins
    suggested: None

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.


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