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Phasit Charoenkwan

Showing results (1-10 of 62) with videos related to

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BMC Bioinformatics|July 28, 2023
StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigensPhasit Charoenkwan, Nalini Schaduangrat, Watshara Shoombuatong
Sensors (Basel, Switzerland)|November 26, 2022
1D Barcode Detection: Novel Benchmark Datasets and Comprehensive Comparison of Deep Convolutional Neural Network ApproachesTeerawat Kamnardsiri, Phasit Charoenkwan, Chommaphat Malang, et al.
Methods (San Diego, Calif.)|December 21, 2024
Deepstack-ACE: A deep stacking-based ensemble learning framework for the accelerated discovery of ACE inhibitory peptidesPhasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat, et al.
Journal of Molecular Biology|November 7, 2024
Stack-AVP: A Stacked Ensemble Predictor Based on Multi-view Information for Fast and Accurate Discovery of Antiviral PeptidesPhasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat, et al.
Journal of Biomolecular Structure & Dynamics|February 22, 2024
Accelerating the identification of the allergenic potential of plant proteins using a stacked ensemble-learning frameworkPhasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat, et al.
Journal of Cheminformatics|May 6, 2023
DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonistsNalini Schaduangrat, Nuttapat Anuwongcharoen, Phasit Charoenkwan, et al.
Protein Science : a Publication of the Protein Society|July 17, 2025
PSR-MAPMS: A new approach for the interpretable prediction of myelin autoantigenic peptides in multiple sclerosis using multi-source propensity scoresPhasit Charoenkwan, Nalini Schaduangrat, Pramote Chumnanpuen, et al.
Journal of Computer-Aided Molecular Design|June 20, 2020
Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representationPhasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, et al.
Analytical Biochemistry|April 26, 2020
iTTCA-Hybrid: Improved and robust identification of tumor T cell antigens by utilizing hybrid feature representationPhasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, et al.
IEEE Transactions on Computational Biology and Bioinformatics|August 14, 2025
iMRSA-Fuse: A Fast and Accurate Computational Approach for Predicting Anti-MRSA Peptides by Fusing Multi-View InformationPhasit Charoenkwan, Nalini Schaduangrat, Mohammad Ali Moni, et al.
Pageof 7

Showing results (1-10 of 62) with videos related to

Sort By:
Pageof 7
BMC Bioinformatics|July 28, 2023
StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigensPhasit Charoenkwan, Nalini Schaduangrat, Watshara Shoombuatong
Sensors (Basel, Switzerland)|November 26, 2022
1D Barcode Detection: Novel Benchmark Datasets and Comprehensive Comparison of Deep Convolutional Neural Network ApproachesTeerawat Kamnardsiri, Phasit Charoenkwan, Chommaphat Malang, et al.
Methods (San Diego, Calif.)|December 21, 2024
Deepstack-ACE: A deep stacking-based ensemble learning framework for the accelerated discovery of ACE inhibitory peptidesPhasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat, et al.
Journal of Molecular Biology|November 7, 2024
Stack-AVP: A Stacked Ensemble Predictor Based on Multi-view Information for Fast and Accurate Discovery of Antiviral PeptidesPhasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat, et al.
Journal of Biomolecular Structure & Dynamics|February 22, 2024
Accelerating the identification of the allergenic potential of plant proteins using a stacked ensemble-learning frameworkPhasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat, et al.
Journal of Cheminformatics|May 6, 2023
DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonistsNalini Schaduangrat, Nuttapat Anuwongcharoen, Phasit Charoenkwan, et al.
Protein Science : a Publication of the Protein Society|July 17, 2025
PSR-MAPMS: A new approach for the interpretable prediction of myelin autoantigenic peptides in multiple sclerosis using multi-source propensity scoresPhasit Charoenkwan, Nalini Schaduangrat, Pramote Chumnanpuen, et al.
Journal of Computer-Aided Molecular Design|June 20, 2020
Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representationPhasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, et al.
Analytical Biochemistry|April 26, 2020
iTTCA-Hybrid: Improved and robust identification of tumor T cell antigens by utilizing hybrid feature representationPhasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, et al.
IEEE Transactions on Computational Biology and Bioinformatics|August 14, 2025
iMRSA-Fuse: A Fast and Accurate Computational Approach for Predicting Anti-MRSA Peptides by Fusing Multi-View InformationPhasit Charoenkwan, Nalini Schaduangrat, Mohammad Ali Moni, et al.
Pageof 7