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  1. 1
    Numbers of predictions.

    Numbers of predictions. by Bjørn André Bredesen-Aa (13767126)

    Published 2022
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  2. 2
    Predictions at the A) <i>invected</i> and B) <i>vestigial</i> loci.

    Predictions at the A) <i>invected</i> and B) <i>vestigial</i> loci. by Bjørn André Bredesen-Aa (13767126)

    Published 2022
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  3. 3
    Prediction overlap with experimental data.

    Prediction overlap with experimental data. by Bjørn André Bredesen-Aa (13767126)

    Published 2022
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  4. 4
    Modelling COVID-19 evolution and control in Portugal: Code and data from 2 of March to 19 August 2020.

    Modelling COVID-19 evolution and control in Portugal: Code and data from 2 of March to 19 August 2020. by Ricardo Jorge Pais (3627494)

    Published 2020
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  5. 5
    Multiprocessing and GPU application of SVMs significantly reduces run-times.

    Multiprocessing and GPU application of SVMs significantly reduces run-times. by Bjørn André Bredesen-Aa (13767126)

    Published 2022
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  6. 6
    Deep-MOCCA schematic.

    Deep-MOCCA schematic. by Bjørn André Bredesen-Aa (13767126)

    Published 2022
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  7. 7
    Core data preparation features.

    Core data preparation features. by Bjørn André Bredesen-Aa (13767126)

    Published 2022
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  8. 8
    Models.

    Models. by Bjørn André Bredesen-Aa (13767126)

    Published 2022
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  9. 9
    Cross-validation Precision/Recall curves.

    Cross-validation Precision/Recall curves. by Bjørn André Bredesen-Aa (13767126)

    Published 2022
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  10. 10
    Sequence feature analysis.

    Sequence feature analysis. by Bjørn André Bredesen-Aa (13767126)

    Published 2022
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  11. 11
    python3 software to reproduce Figs 6, 7 and 8.

    python3 software to reproduce Figs 6, 7 and 8. by Stéphane Plaszczynski (14992817)

    Published 2023
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  12. 12
    FWC python implementation

    FWC python implementation by Roland Mühlenbernd (11755871)

    Published 2021
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  13. 13
    Implementation of the SAACS in python.

    Implementation of the SAACS in python. by Pavel Škrabánek (6106427)

    Published 2019
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  14. 14
    The optimal ProSmith models combine predictions based on the multimodal Transformer Network with predictions based on separate numerical representations of proteins and small molecules.

    The optimal ProSmith models combine predictions based on the multimodal Transformer Network with predictions based on separate numerical representations of proteins and small molec... by Alexander Kroll (9304225)

    Published 2024
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  15. 15
    Python code for estimating parameters of an ALAAM model.

    Python code for estimating parameters of an ALAAM model. by Alex Stivala (8356257)

    Published 2024
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  16. 16
    For accurate DTA predictions, ProSmith requires training on identical drugs but not on similar proteins.

    For accurate DTA predictions, ProSmith requires training on identical drugs but not on similar proteins. by Alexander Kroll (9304225)

    Published 2024
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  17. 17
    Enzyme-substrate pair predictions improve with increasing substrate similarity compared to training substrates.

    Enzyme-substrate pair predictions improve with increasing substrate similarity compared to training substrates. by Alexander Kroll (9304225)

    Published 2024
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  18. 18
    Drug-target affinity predictions improve with increasing drug similarity score compared to the training drugs.

    Drug-target affinity predictions improve with increasing drug similarity score compared to the training drugs. by Alexander Kroll (9304225)

    Published 2024
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  19. 19
    Tools used in implementing the RNN models.

    Tools used in implementing the RNN models. by Fahmi H. Quradaa (17896527)

    Published 2024
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  20. 20
    Python code that implements the Hoshen Kopleman algorithm used to identify the clusters shown in Figs 9 and 10.

    Python code that implements the Hoshen Kopleman algorithm used to identify the clusters shown in Figs 9 and 10. by Leonardo Costa Ribeiro (10439243)

    Published 2023
    “…<p>Python code that implements the Hoshen Kopleman algorithm used to identify the clusters shown in Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0285630#pone.0285630.g009" target="_blank">9</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0285630#pone.0285630.g010" target="_blank">10</a>.…”
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