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Showing 61 - 80 results of 10,252 for search '(( algorithm gene function ) OR ((( algorithm python function ) OR ( algorithm which function ))))', query time: 0.56s Refine Results
  1. 61
    GO analysis bubble plot for other four modules.

    GO analysis bubble plot for other four modules. by Zongjin Li (38031)

    Published 2025
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  2. 62
    R-squared comparison of test function.

    R-squared comparison of test function. by Kejia Liu (5699651)

    Published 2025
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  3. 63
    Illustration of the perturbation of the protein relationship network and NECARE algorithm.

    Illustration of the perturbation of the protein relationship network and NECARE algorithm. by Jiajun Qiu (4684921)

    Published 2021
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  4. 64
    PyNoetic’s stimuli generation and recording module, which supports both ERP and SSVEP.

    PyNoetic’s stimuli generation and recording module, which supports both ERP and SSVEP. by Gursimran Singh (575288)

    Published 2025
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  5. 65
    DataSheet_1_Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms.docx

    DataSheet_1_Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms.docx by Hang Zhou (230977)

    Published 2023
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  6. 66
    PyNoetic’s pre-processing module, which supports filtering and artifact removal, including ICA.

    PyNoetic’s pre-processing module, which supports filtering and artifact removal, including ICA. by Gursimran Singh (575288)

    Published 2025
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  7. 67
    F1 obtained for PFP-WGAN and DeepGO-Seq for GO terms as a function of number of available positive training samples.

    F1 obtained for PFP-WGAN and DeepGO-Seq for GO terms as a function of number of available positive training samples. by Seyyede Fatemeh Seyyedsalehi (10196767)

    Published 2021
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  8. 68
    Gene content overlap in <i>RNF43 G659fs</i> mutation-specific pathways.

    Gene content overlap in <i>RNF43 G659fs</i> mutation-specific pathways. by Alexis M. Thornton (11068403)

    Published 2021
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  9. 69
    LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes

    LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes by Olivier Collier (7444844)

    Published 2019
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  10. 70
    Comparison of algorithms in two cases.

    Comparison of algorithms in two cases. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  11. 71
    Flow of the NSGA-II algorithm.

    Flow of the NSGA-II algorithm. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  12. 72
    A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data

    A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data by Anthony S. Barente (14035175)

    Published 2022
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  13. 73
    RNF43 truncating mutations have similar transcriptional profiles to BRAF missense mutations.

    RNF43 truncating mutations have similar transcriptional profiles to BRAF missense mutations. by Alexis M. Thornton (11068403)

    Published 2021
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  14. 74
    Genetic Algorithm (GA) and CAGE-based personalization block diagrams.

    Genetic Algorithm (GA) and CAGE-based personalization block diagrams. by Dmitrii Smirnov (8822324)

    Published 2020
    “…<p>(<b>A</b>) Genetic algorithm schematic diagram. Initially a set of organisms is generated, each of which is determined by a random vector of scaling factors for optimized model parameters (step 1). …”
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  15. 75
    Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, the temporal molecular states <i>n</i> at specific time <i>t</i> will be generated for further visualization.

    Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

    Published 2025
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  16. 76
    DataSheet1_Bayesian hidden mark interaction model for detecting spatially variable genes in imaging-based spatially resolved transcriptomics data.PDF

    DataSheet1_Bayesian hidden mark interaction model for detecting spatially variable genes in imaging-based spatially resolved transcriptomics data.PDF by Jie Yang (121778)

    Published 2024
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  17. 77
    PyNoetic’s channel selection module, which supports various channel selection criteria including CSP, correlation, Mutual Information, and Chi-squared.

    PyNoetic’s channel selection module, which supports various channel selection criteria including CSP, correlation, Mutual Information, and Chi-squared. by Gursimran Singh (575288)

    Published 2025
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  18. 78
    How fries chooses which sequences to keep and which sequences to prune.

    How fries chooses which sequences to keep and which sequences to prune. by Anna U. Lowegard (8051597)

    Published 2020
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  19. 79
    GS and MM calculation results for each gene.

    GS and MM calculation results for each gene. by Guanghao Xin (17583227)

    Published 2023
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  20. 80
    Results of the replicated simulations.

    Results of the replicated simulations. by Susana Conde (5495675)

    Published 2024
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