Showing 101 - 120 results of 4,116 for search '(( algorithm ((protein function) OR (python function)) ) OR ( algorithm using function ))', query time: 0.20s Refine Results
  1. 101

    Table 4_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

    Published 2025
    “…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”
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    Table 5_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

    Published 2025
    “…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”
  3. 103

    Boxplot analysis for ITAE objective function using en-CSA, CSA, RUN, PDO and RIME algorithms. by Sarah A. Alzakari (19704611)

    Published 2024
    “…<p>Boxplot analysis for ITAE objective function using en-CSA, CSA, RUN, PDO and RIME algorithms.…”
  4. 104

    Supplemental files to the study "Limitations of Current Machine-Learning Models in Predicting Enzymatic Functions for Uncharacterized Proteins" by Valerie de Crecy-Lagard (12823436)

    Published 2025
    “…An open question is the ability of machine-learning approaches to predict enzymatic functions unseen in the training sets. Using a set of <i>E. coli</i> unknowns, we evaluated the current state-of-the-art machine-learning approaches and found that these methods currently lack the ability to integrate scientific reasoning into their prediction algorithms. …”
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    Quantum Computing and peptide folding by Akshay Uttarkar (19699990)

    Published 2024
    “…<p dir="ltr">The work "Peptide Folding with Quantum CVaR-VQE Algorithm" represents a significant advancement in the field of computational biology, particularly in the challenging domain of protein folding. …”
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    Supplemental Tables S1 and S2 for Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks by Diana Murray (16859046)

    Published 2025
    “…<p dir="ltr">We report on the integration of three methods that are computationally efficient enough to predict, on a proteome-wide scale, whether two proteins are likely to form a binary complex. The methods include PrePPI, which uses three-dimensional structure information as a basis for predictions, Topsy-Turvy, which analyzes sequences using a protein language model, and ZEPPI, which uses evolutionary information to evaluate protein-protein interfaces. …”
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    WCST scores and protein expression levels. by Chen Chen (6544)

    Published 2025
    “…This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. …”
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