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Journal of Hazardous Materials|November 26, 2025
Mulaqua: An interpretable multimodal deep learning framework for identifying PMT/vPvM substances in drinking waterNguyen Doan Hieu Nguyen, Nhat Truong Pham, Hojin Seo, et al.
Computers in Biology and Medicine|December 25, 2023
Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approachMd Harun-Or-Roshid, Kazuhiro Maeda, Le Thi Phan, et al.
International Journal of Molecular Sciences|April 25, 2019
mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer PeptidesVinothini Boopathi, Sathiyamoorthy Subramaniyam, Adeel Malik, et al.
Computers in Biology and Medicine|June 17, 2022
NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptidesPhasit Charoenkwan, Nalini Schaduangrat, Pietro Lio', et al.
Computers in Biology and Medicine|June 11, 2022
SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteinsPhasit Charoenkwan, Nalini Schaduangrat, Mohammad Ali Moni, et al.
Iscience|September 1, 2022
Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning frameworkPhasit Charoenkwan, Nalini Schaduangrat, Pietro Lio', et al.
Briefings in Bioinformatics|May 8, 2021
StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptidesPhasit Charoenkwan, Wararat Chiangjong, Chanin Nantasenamat, et al.
Frontiers in Immunology|August 14, 2018
iBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope PredictionBalachandran Manavalan, Rajiv Gandhi Govindaraj, Tae Hwan Shin, et al.
Molecular Therapy. Nucleic Acids|May 23, 2024
ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learningNhat Truong Pham, Annie Terrina Terrance, Young-Jun Jeon, et al.
Briefings in Bioinformatics|July 17, 2026
Decoding apoptosis, ferroptosis, and inflammatory cell death in adenomyosis at single-cell resolutionQingjing Sheng, Qiongwei Wu, Jiao Fan, et al.
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