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Showing 1 - 20 results of 37 for search '(( binary basic process optimization algorithm ) OR ( less based library optimization algorithm ))*', query time: 0.48s Refine Results
  1. 1
    Train test validation libraries.

    Train test validation libraries. by Muhammad Usman Tariq (11022141)

    Published 2021
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  2. 2
    Closed search (±5ppm) comparison against Crux and MSFragger using real-world data from PRIDE repository PXD000612 (top), PXD009861 (middle), and PXD001468 (bottom).

    Closed search (±5ppm) comparison against Crux and MSFragger using real-world data from PRIDE repository PXD000612 (top), PXD009861 (middle), and PXD001468 (bottom). by Muhammad Usman Tariq (11022141)

    Published 2021
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  3. 3
    Training parameters for SpeCollate.

    Training parameters for SpeCollate. by Muhammad Usman Tariq (11022141)

    Published 2021
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  4. 4
    Peptides.

    Peptides. by Muhammad Usman Tariq (11022141)

    Published 2021
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  5. 5
    Fig 9 -

    Fig 9 - by Muhammad Usman Tariq (11022141)

    Published 2021
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  6. 6
    UMAP.

    UMAP. by Muhammad Usman Tariq (11022141)

    Published 2021
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  7. 7
    Process of generating MS/MS spectra from a protein mixture using mass-spectrometry analysis.

    Process of generating MS/MS spectra from a protein mixture using mass-spectrometry analysis. by Muhammad Usman Tariq (11022141)

    Published 2021
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  8. 8
    Modifications and the character values used in the training data.

    Modifications and the character values used in the training data. by Muhammad Usman Tariq (11022141)

    Published 2021
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  9. 9
    2D UMAP visualization of embedded spectra and peptide vectors generated by SpeCollate.

    2D UMAP visualization of embedded spectra and peptide vectors generated by SpeCollate. by Muhammad Usman Tariq (11022141)

    Published 2021
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  10. 10
    Fig 6 -

    Fig 6 - by Muhammad Usman Tariq (11022141)

    Published 2021
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  11. 11
    Online sextuplet mining for SNAP-loss.

    Online sextuplet mining for SNAP-loss. by Muhammad Usman Tariq (11022141)

    Published 2021
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  12. 12
    SpeCollate: Deep Similarity Network for proteomics.

    SpeCollate: Deep Similarity Network for proteomics. by Muhammad Usman Tariq (11022141)

    Published 2021
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  13. 13
    A generic proteomics flow.

    A generic proteomics flow. by Muhammad Usman Tariq (11022141)

    Published 2021
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  14. 14
    Space transition methods.

    Space transition methods. by Muhammad Usman Tariq (11022141)

    Published 2021
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  15. 15
    Characteristics of training dataset used.

    Characteristics of training dataset used. by Muhammad Usman Tariq (11022141)

    Published 2021
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  16. 16
    Venn diagrams showing the overlap of peptides among SpeCollate, Crux, and MSFragger.

    Venn diagrams showing the overlap of peptides among SpeCollate, Crux, and MSFragger. by Muhammad Usman Tariq (11022141)

    Published 2021
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  17. 17
    Datasets.

    Datasets. by Muhammad Usman Tariq (11022141)

    Published 2021
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  18. 18
    Fig 7 -

    Fig 7 - by Muhammad Usman Tariq (11022141)

    Published 2021
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  19. 19
    Using Variable Data-Independent Acquisition for Capillary Electrophoresis-Based Untargeted Metabolomics

    Using Variable Data-Independent Acquisition for Capillary Electrophoresis-Based Untargeted Metabolomics by Saki Kiuchi (19374255)

    Published 2024
    “…Moreover, we conducted MT prediction for metabolites recorded in mass spectral libraries and metabolite structure databases containing a total of 469,870 compounds, achieving an accuracy of less than 1.5 min root mean squares. …”
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  20. 20
    Using Variable Data-Independent Acquisition for Capillary Electrophoresis-Based Untargeted Metabolomics

    Using Variable Data-Independent Acquisition for Capillary Electrophoresis-Based Untargeted Metabolomics by Saki Kiuchi (19374255)

    Published 2024
    “…Moreover, we conducted MT prediction for metabolites recorded in mass spectral libraries and metabolite structure databases containing a total of 469,870 compounds, achieving an accuracy of less than 1.5 min root mean squares. …”
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