Browsing "Physiology" by Keyword : Machine Learning

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Showing results 12 to 21 of 21

Pub YearTitleAJOU Author(s)
2018Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved AccuracyBalachandran, Manavalan, 신태환, 이광
2019mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representationBalachandran, Manavalan, Basith, Shaherin, 신태환, 이광
2021Meta-i6mA: An interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning frameworkBalachandran, Manavalan, Basith, Shaherin, 이광
2021NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learningBalachandran, Manavalan
2022Recent Trends on the Development of Machine Learning Approaches for the Prediction of Lysine Acetylation SitesBasith, Shaherin, 이광, 장혜진
2024SEP-AlgPro: An efficient allergen prediction tool utilizing traditional machine learning and deep learning techniques with protein language model featuresBasith, Shaherin, 이광
2021StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptidesBalachandran, Manavalan
2022STALLION: A stacking-based ensemble learning framework for prokaryotic lysine acetylation site predictionBalachandran, Manavalan, Basith, Shaherin, 이광
2022THRONE: A New Approach for Accurate Prediction of Human RNA N7-Methylguanosine SitesBasith, Shaherin, 이광
2021Umpred-frl: A new approach for accurate prediction of umami peptides using feature representation learningBalachandran, Manavalan
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