Showing 1 - 20 results of 1,360 for search '(( algorithm protein function ) OR ( ((algorithm python) OR (algorithms within)) function ))*', query time: 0.38s Refine Results
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    <b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

    Published 2025
    “…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…”
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    NRPStransformer, an Accurate Adenylation Domain Specificity Prediction Algorithm for Genome Mining of Nonribosomal Peptides by Zhihan Zhang (1403308)

    Published 2025
    “…Leveraging the sequences within the flavodoxin-like subdomain, we developed a substrate specificity prediction algorithm using a protein language model, achieving 92% overall prediction accuracy for 43 frequently observed amino acids, significantly improving the prediction reliability. …”
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    Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

    Published 2025
    “…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”
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    Core genes were selected through PPI analysis based on three algorithms. by Dong-Hee Han (140305)

    Published 2024
    “…<p>Among 46 DEGs whose expression significantly changed in A549 and BEAS-2B cell lines, the core genes were selected using three algorithms: MCC, MNC, and DEGREE. <b>(A)</b> The top 10 genes were prioritized using MCC analysis, which identifies the largest clique within the PPI network and selects the central proteins within the clique. …”
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    Morpheus: A fragment-based algorithm to predict metamorphic behaviour in proteins across proteomes by Vijay Subramanian (20718933)

    Published 2025
    “…<p dir="ltr">Supplementary material for Morpheus: A fragment-based algorithm to predict metamorphic behaviour in proteins across proteomes.…”
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    Identification and functional analysis of hub genes. by Wei Ya Lan (22403712)

    Published 2025
    “…(C, D) Top 10 hub genes identified using the Maximal Clique Centrality (MCC) algorithm; darker colors indicate higher centrality within the PPI network. …”
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    EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit by Gonzalo Colmenarejo (650249)

    Published 2025
    “…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …”
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