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Showing 81 - 100 results of 178 for search '(( python ((code implementation) OR (effective implementation)) ) OR ( python code implemented ))', query time: 0.42s Refine Results
  1. 81
    High-Throughput Mass Spectral Library Searching of Small Molecules in R with NIST MSPepSearch

    High-Throughput Mass Spectral Library Searching of Small Molecules in R with NIST MSPepSearch by Andrey Samokhin (20282728)

    Published 2025
    “…Despite the availability of numerous library search algorithms, those developed by NIST and implemented in MS Search remain predominant, partly because commercial databases (e.g., NIST, Wiley) are distributed in proprietary formats inaccessible to custom code. …”
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  2. 82
    Comparison data 7 for <i>Lamprologus ocellatus</i>.

    Comparison data 7 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  3. 83
    Sample data for <i>Neolamprologus multifasciatus</i>.

    Sample data for <i>Neolamprologus multifasciatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  4. 84
    Sample data for <i>Lamprologus ocellatus</i>.

    Sample data for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  5. 85
    Comparison data 3 for <i>Lamprologus ocellatus</i>.

    Comparison data 3 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  6. 86
    Sample data for <i>Telmatochromis temporalis</i>.

    Sample data for <i>Telmatochromis temporalis</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  7. 87
    Comparison data 4 for <i>Lamprologus ocellatus</i>.

    Comparison data 4 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  8. 88
    Comparison data 1 for <i>Lamprologus ocellatus</i>.

    Comparison data 1 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  9. 89
    Comparison data 2 for <i>Lamprologus ocellatus</i>.

    Comparison data 2 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  10. 90
    Comparison data 5 for <i>Lamprologus ocellatus</i>.

    Comparison data 5 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  11. 91
    Comparison data 6 for <i>Lamprologus ocellatus</i>.

    Comparison data 6 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  12. 92
    Bursty model and Refractory model simulation results. (A) Bursty model simulation results ( samples). The production of nascent mRNA occurs in bursts and degrade after delay time <i>τ</i>. The burst frequency , the mean burst size and . (B) Refractory model simulation results ( samples). The gene has three states, , and , and can switch from one state to another. Moreover, nascent mRNA is produced only when the gene is in state . The parameters are , , , and <i>τ</i> = 1.

    Bursty model and Refractory model simulation results. (A) Bursty model simulation results ( samples). The production of nascent mRNA occurs in bursts and degrade after delay time <... by Ziyan Jin (21021976)

    Published 2025
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  13. 93
    RNA Velocity model simulation results. (A) Simulation results for RNA Velocity model without delay ( samples). The simulation is composed of an up-regulation phase (both production and degradation) and a down-regulation phase (degradation-only). In the first phase, <i>%</i>, <i>%</i> and <i>%</i> for the reaction time . In the second phase,  ∕ 1, <i>X</i> ∼ NB ( <i>r</i> , <i>μ</i> )  and <i>μ</i>. (B) Simulation results for RNA Velocity model with delay in degradation ( samples). RNA degradation is specified as a delayed reaction. In the first (up-regulation) phase: <i>Y</i> ≡ <i>n</i>, <i>t</i> = 0 and <i>%</i>. In the second (down-regulation) phase: <i>%</i>, <i>%</i> and <i>%.</i>

    RNA Velocity model simulation results. (A) Simulation results for RNA Velocity model without delay ( samples). The simulation is composed of an up-regulation phase (both production... by Ziyan Jin (21021976)

    Published 2025
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  14. 94
    Modeling lung cancer AST network and its perturbation with a SOX2 degrader. (A) Lung Cancer AST Network and the corresponding stochastic equations. Parameters are , , , , , , , , with different . The control parameter is . (B) The potential landscape is approximated by simulating a large number of molecules corresponding to concentrations ( samples) with parameter values , , and , respectively representing the adenocarcinoma state, the intermediate state, and the squamous carcinoma state. FOXA2 and SOX2 amounts are chosen to visualize the landscape. (C) Principle of SOX2-targeted degradation by the PROTAC technique. The PROTAC molecule here is designed to recognize SOX2 proteins and to recruit E3 ligases to induce an addition of ubiquitin (Ub) proteins onto SOX2, which causes degradation of SOX2 subsequently. (D) Application of a SOX2 degrader to the squamous carcinoma state () reprogram the AST network back to the adenocarcinoma state. Here the treatment effect of the SOX2 degrad

    Modeling lung cancer AST network and its perturbation with a SOX2 degrader. (A) Lung Cancer AST Network and the corresponding stochastic equations. Parameters are , , , , , , , , w... by Ziyan Jin (21021976)

    Published 2025
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  15. 95
    Memory monitoring recognition test workflow.

    Memory monitoring recognition test workflow. by Pedro C. Martínez-Suárez (21192459)

    Published 2025
    “…</p><p>Method</p><p>The MMRT was developed using Python and Kivy, facilitating the creation of cross-platform user interfaces. …”
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  16. 96
    Voice recognition workflow.

    Voice recognition workflow. by Pedro C. Martínez-Suárez (21192459)

    Published 2025
    “…</p><p>Method</p><p>The MMRT was developed using Python and Kivy, facilitating the creation of cross-platform user interfaces. …”
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  17. 97
    Memory monitoring recognition test main screen.

    Memory monitoring recognition test main screen. by Pedro C. Martínez-Suárez (21192459)

    Published 2025
    “…</p><p>Method</p><p>The MMRT was developed using Python and Kivy, facilitating the creation of cross-platform user interfaces. …”
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  18. 98
    Task descriptions.

    Task descriptions. by Pedro C. Martínez-Suárez (21192459)

    Published 2025
    “…</p><p>Method</p><p>The MMRT was developed using Python and Kivy, facilitating the creation of cross-platform user interfaces. …”
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  19. 99
    Map of cumulative daily root-mean-square errors for each province in China in the test set.

    Map of cumulative daily root-mean-square errors for each province in China in the test set. by Xiaoyi Wang (90589)

    Published 2025
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  20. 100
    Summary of Tourism Dataset.

    Summary of Tourism Dataset. by Jing Zhang (23775)

    Published 2025
    “…The model employs robust forecasting of economic impacts, visitor spending patterns, and behavior while accounting for uncertainty through variational inference. The implementation uses Python language on a tourism dataset comprising necessary attributes like visitor numbers, days, spending patterns, employment, international tourism samples over a specific region, and a diverse age group analyzed over a year. …”
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