Showing 201 - 220 results of 12,434 for search '(((( algorithm pre function ) OR ( algorithm 1 function ))) OR ( algorithm python function ))', query time: 0.52s Refine Results
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    The ALO algorithm optimization flowchart. by Wenjing Wang (181404)

    Published 2024
    “…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”
  4. 204

    The IALO algorithm solution flowchart. by Wenjing Wang (181404)

    Published 2024
    “…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”
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    Objective functions (i.e., RMSEs) and time of optimization for different population sizes of the used algorithms. by Yao Peng (1928524)

    Published 2023
    “…<p>Objective functions (i.e., RMSEs) and time of optimization for different population sizes of the used algorithms.…”
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    Algorithm pseudocode. by Cheng-jie Chen (22272090)

    Published 2025
    “…The experimental results show that the proposed method enhances the image with a PCQI of 1.033, an IQE of 0.610, an IQM of 1.830, and an information entropy higher than 0.7. …”
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    Glioblastoma multiforme (GBM) pathway modules identified by the netboxr package from cancer genomics alteration data without the use of pre-defined gene sets. by Eric Minwei Liu (9592176)

    Published 2020
    “…For more detailed understanding users should also inspect the function of the genes contained in the modules. In this glioblastoma example, the largest module (M1, light orange background) contains genes related to the PIK3 pathway and functions related to AKT signaling (also known as “PKB signaling” in the GO Gene Ontology). …”
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    Efficient algorithms to discover alterations with complementary functional association in cancer by Rebecca Sarto Basso (6728921)

    Published 2019
    “…We provide analytic evidence of the effectiveness of UNCOVER in finding high-quality solutions and show experimentally that UNCOVER finds sets of alterations significantly associated with functional targets in a variety of scenarios. In particular, we show that our algorithms find sets which are better than the ones obtained by the state-of-the-art method, even when sets are evaluated using the statistical score employed by the latter. …”
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    Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling by Giacomo Janson (8138517)

    Published 2019
    “…This program implements the “modeling by satisfaction of spatial restraints” strategy and its core algorithm has not been altered significantly since the early 1990s. …”
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