Full publication list at (Google Scholar Profile)
Published in npj Digital Medicine, 2026
Recommended citation: Kha Q.H., Nguyen D.Q., Hoang P.P.V., Huynh U.K., Pham K.D., Phung M.T., Huynh T.P., Le H.B., Vo N.P., Do M.H., Le K.T.Q., Pham N.T., Le H.G., Nguyen K.P., Nguyen P., Nguyen T.H., Huynh P.K., Le M.H.N., Xu M., & Le N.Q.K. (2026). Robust Prediction of Drug Interactions using Chemical Descriptors. npj Digital Medicine. https://doi.org/10.1038/s41746-026-03025-2
Published in Bioinformatics, 2026
Recommended citation: Liu A.Z.R., Le N.Q.K., & Chua M.C.H. (2026). 3DICE: Interpretable 3D Cross-Modal Learning for Drug–Target Interaction Prediction and Large-Scale Drug Discovery. Bioinformatics, btag488. https://doi.org/10.1093/bioinformatics/btag488
Published in Bioinformatics, 2026
Recommended citation: Tay D.Y.J., Le N.Q.K., & Chua M.C.H. (2026). Bayesian hyperparameter optimization improves scGPT fine-tuning for single-cell multi-omics integration. Bioinformatics, 42(7), btag374. https://doi.org/10.1093/bioinformatics/btag374
Published in Cancer Genetics, 2026
Recommended citation: Yang R., Le N.Q.K., & Chua M.C.H. (2026). Enhancing cell type annotation for cancer transcriptomics using retrieval-augmented generation. Cancer Genetics, 306–307, 7-16. https://doi.org/10.1016/j.cancergen.2026.05.002
Published in International Journal of Biological Macromolecules, 2026
Recommended citation: Chang T., Le N.Q.K., & Chua M.C.H. (2026). RIMGOGraph: integrating AlphaFold-derived residue interaction graphs and protein language embeddings for structure-informed protein function prediction. International Journal of Biological Macromolecules, 367, 152509. https://doi.org/10.1016/j.ijbiomac.2026.152509
Published in Briefings in Bioinformatics, 2026
Recommended citation: Vo T.H., & Le N.Q.K. (2026). Toward trustworthy artificial intelligence in multi-omics: a review of reproducibility, stability, and interpretability. Briefings in Bioinformatics, 27(3), bbag227. https://doi.org/10.1093/bib/bbag227
Published in Physics in Medicine & Biology, 2026
Recommended citation: Kha Q.H., Nguyen P.K., Le M.H.N., Huynh H.H., Huynh P.K., Nguyen D., Nguyen L.H., Le H.V., Le T.D.T., Le H.H., Le N.H.H., Nguyen T., Nguyen T.H., Xu M., & Le N.Q.K. (2026). A CT radiomics signature enables risk stratification and survival prediction in colorectal liver metastases. Physics in Medicine & Biology, 71(5), 055012. https://doi.org/10.1088/1361-6560/ae4c14
Published in Briefings in Bioinformatics, 2026
Recommended citation: Nguyen T.L., Kha H.Q., Nguyen P.K., Le M.H.N., Le D.T., & Le N.Q.K. (2026). EPINTLM: enhancer–promoter prediction with pretrained k-mer embeddings and residual cross-attention. Briefings in Bioinformatics, 27(1), bbag064. https://doi.org/10.1093/bib/bbag064
Published in Journal of Translational Medicine, 2025
Recommended citation: Nguyen M.H., Do-Huu H.H., Nguyen P.T., Tran N.D., Linh N.T., Le H., & Le N.Q.K. (2026). Translational deep learning models for risk stratification to predict prognosis and immunotherapy response in gastric cancer using digital pathology. Journal of Translational Medicine, 23, 1419. https://doi.org/10.1186/s12967-025-07416-z
Published in IEEE Journal of Biomedical and Health Informatics, 2025
Recommended citation: Le M.H.N., Le T.Q.K., Nguyen T.H., Nguyen D., Nguyen H.Q., Le N.H.H., Nguyen K.D., Kha H.Q., Nguyen P.K., Le H.H., Huynh H.H., Ho D.M., Nguyen T.M., Nguyen Q., Xu M., Huynh P.K., & Le N.Q.K. (2026). OASIS-Net: An Obstetric Adversarial Semi-Supervised Image Segmentation Network for Cervical and Fetal Head Ultrasound Imaging. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2025.3631102
Published in Computerized Medical Imaging and Graphics, 2025
Recommended citation: Zhao Z., Le N.Q.K., & Chua M.C.H. (2025). AI-driven multi-modal framework for prognostic modeling in glioblastoma: Enhancing clinical decision support. Computerized Medical Imaging and Graphics, 124, 102628. https://doi.org/10.1016/j.compmedimag.2025.102628
Published in Evidence-Based Dentistry, 2025
Recommended citation: Lam Q.T., Le M.H.N., Lee I.T., & Le N.Q.K. (2025). Evaluating YOLO for dental caries diagnosis: a systematic review and meta-analysis. Evidence-Based Dentistry, 26, 176. https://doi.org/10.1038/s41432-025-01180-1
Published in Lung Cancer, 2025
Recommended citation: Nguyen M.H., Le M.H.N., Bui A.T., & Le N.Q.K. (2025). Artificial intelligence in predicting EGFR mutations from whole slide images in lung Cancer: A systematic review and Meta-Analysis. Lung Cancer, 204, 108577. https://doi.org/10.1016/j.lungcan.2025.108577
Published in Journal of Medical Systems, 2025
Recommended citation: Tran T.O., Nguyen T.H., Nguyen T.T., & Le N.Q.K. (2025). MLG2Net: Molecular Global Graph Network for Drug Response Prediction in Lung Cancer Cell Lines. Journal of Medical Systems, 49, 47. https://doi.org/10.1007/s10916-025-02182-3
Published in Molecular Therapy Nucleic Acids, 2025
Recommended citation: Le N.Q.K. (2025). Unlocking RNA mysteries: Predicting subcellular localizations with AI. Molecular Therapy Nucleic Acids, 36(1), 102481. https://doi.org/10.1016/j.omtn.2025.102481
Published in Journal of Medical Systems, 2025
Recommended citation: Le V.H., Minh T.N.T., Kha Q.H., & Le N.Q.K. (2025). Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images. Journal of Medical Systems, 49(1), 22. https://doi.org/10.1007/s10916-025-02156-5
Published in IEEE Journal of Biomedical and Health Informatics, 2024
Recommended citation: Nguyen D., Le M.H.N., Le T.Q., Charles-Okezie C., Diaz M.J., Sabet C., Dang H.T., Nguyen T., Nguyen H., Tran M., Le N.Q.K., Muncey A., & Huynh P.K. (2024). Deep Learning-based Integrated System for Intraoperative Blood Loss Quantification in Surgical Sponges. IEEE Journal of Biomedical and Health Informatics, 29(9), 6342-6352. https://doi.org/10.1109/JBHI.2024.3499852
Published in Journal of Assisted Reproduction and Genetics, 2024
Recommended citation: Luong T.M.T., Ho N.T., Hwu Y.M., Lin S.Y., Ho J.Y.P., Wang R.S., Lee Y.X., Tan S.J., Lee Y.R., Huang Y.L., Le N.Q.K., & Tzeng C.R. (2024). Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation. Journal of Assisted Reproduction and Genetics, 41, 2349–2358. https://doi.org/10.1007/s10815-024-03178-7
Published in Journal of Imaging Informatics in Medicine, 2024
Recommended citation: Dang L.H., Hung S.H., Le N.T.N., Chuang W.K., Wu J.Y., Huang T.C., & Le N.Q.K. (2024). Enhancing Nasopharyngeal Carcinoma Survival Prediction: Integrating Pre- and Post-Treatment MRI Radiomics with Clinical Data. Journal of Imaging Informatics in Medicine, 37, 2474–2489. https://doi.org/10.1007/s10278-024-01109-7
Published in Computers in Biology and Medicine, 2024
Recommended citation: Tran T.O., & Le N.Q.K. (2024). Sa-TTCA: An SVM-based approach for tumor T-cell antigen classification using features extracted from biological sequencing and natural language processing. Computers in Biology and Medicine, 174, 108408. https://doi.org/10.1016/j.compbiomed.2024.108408
Published in Journal of Imaging Informatics in Medicine, 2024
Recommended citation: Binh L.N., Nhu N.T., Vy V.P.T., Son D.L.H., Hung T.N.K., Bach N., Huy H.Q., Tuan L.V., Le N.Q.K., & Kang J.H. (2024). Multi-Class Deep Learning Model for Detecting Pediatric Distal Forearm Fractures Based on the AO/OTA Classification. Journal of Imaging Informatics in Medicine, 37, 725–733. https://doi.org/10.1007/s10278-024-00968-4
Published in European Radiology, 2024
Recommended citation: Le N.Q.K. (2024). Hematoma expansion prediction: still navigating the intersection of deep learning and radiomics. European Radiology, 34, 2905–2907. https://doi.org/10.1007/s00330-024-10586-x
Published in Nature Computational Science, 2023
Recommended citation: Le N.Q.K. (2023). Predicting emerging drug interactions using GNNs. Nature Computational Science, 3, 1007–1008. https://doi.org/10.1038/s43588-023-00555-7
Published in Computers in Biology and Medicine, 2023
Recommended citation: Singh S., Le N.Q.K., & Wang C. (2024). VF-Pred: Predicting virulence factor using sequence alignment percentage and ensemble learning models. Computers in Biology and Medicine, 168, 107662. https://doi.org/10.1016/j.compbiomed.2023.107662
Published in Briefings in Bioinformatics, 2023
Recommended citation: Le N.Q.K., Li W., & Cao Y. (2023). Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection. Briefings in Bioinformatics, 24(5), bbad319. https://doi.org/10.1093/bib/bbad319
Published in PROTEOMICS, 2023
Recommended citation: Le N.Q.K.. (2023). Leveraging transformers-based language models in proteome bioinformatics. PROTEOMICS, 2300011. https://doi.org/10.1002/pmic.202300011
Published in Biomedical Signal Processing and Control, 2023
Recommended citation: Minh T.N.T., Le V.H., & Le N.Q.K.. (2023). Diffusion-tensor imaging and dynamic susceptibility contrast MRIs improve radiomics-based machine learning model of MGMT promoter methylation status in glioblastomas. Biomedical Signal Processing and Control, 86, Part A, 105122. https://doi.org/10.1016/j.bspc.2023.105122
Published in Academic Radiology, 2023
Recommended citation: Nguyen H.S., Ho D.K.N., Nguyen N.N., Tran H.M., Tam K.W., & Le N.Q.K.. (2024). Predicting EGFR Mutation Status in Non–Small Cell Lung Cancer Using Artificial Intelligence: A Systematic Review and Meta-Analysis. Academic Radiology, 31(2), 660-683. https://doi.org/10.1016/j.acra.2023.03.040
Published in Computational and Structural Biotechnology Journal, 2023
Recommended citation: Tran T.O., Vo T.H., Lam L.H.T., & Le N.Q.K.. (2023). ALDH2 as a potential stem cell-related biomarker in lung adenocarcinoma: Comprehensive multi-omics analysis. Computational and Structural Biotechnology Journal, 21, 1921-1929. https://doi.org/10.1016/j.csbj.2023.02.045
Published in Journal of Digital Imaging, 2023
Recommended citation: Le V.H., Kha Q.H., Minh T.N.T., Nguyen V.H., Le V.L., & Le N.Q.K.. (2023). Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer. Journal of Digital Imaging, 36, 911–922. https://doi.org/10.1007/s10278-023-00778-0
Published in Briefings in Bioinformatics, 2023
Recommended citation: Yuan Q., Chen K., Yu Y., Le N.Q.K., & Chua M.C.H. (2023). Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding. Briefings in Bioinformatics, 24(1), bbac630. https://doi.org/10.1093/bib/bbac630
Published in Chemometrics and Intelligent Laboratory Systems, 2022
Recommended citation: Zheng Z., Le N.Q.K., & Chua M.C.H. (2023). MaskDNA-PGD: an innovative deep learning model for detecting DNA methylation by integrating mask sequences and adversarial PGD training as a data augmentation method. Chemometrics and Intelligent Laboratory Systems, 14(22), 5562. https://doi.org/10.1016/j.chemolab.2022.104715
Published in Journal of Chemical Information and Modeling, 2022
Recommended citation: Kha Q.H., Ho Q.T., & Le N.Q.K. (2022). Identifying SNARE Proteins Using Alignment-Free Method Based on Multi-Scan Convolutional Neural Network and PSSM Profiles. Journal of Chemical Information and Modeling, 62(19), 4820-4826. https://doi.org/10.1021/acs.jcim.2c01034
Published in NMR in Biomedicine, 2022
Recommended citation: Lam L.H.T., Do D.T., Diep D.T.N., Nguyet D.L.N., Truong Q.D., Tri T.T., Thanh H.T., & Le N.Q.K. (2022). Molecular subtype classification of low-grade gliomas using magnetic resonance imaging-based radiomics and machine learning. NMR in Biomedicine, 35(11):e4792. https://doi.org/10.1002/nbm.4792
Published in Journal of Magnetic Resonance Imaging, 2022
Recommended citation: Hung T.N.K., Vy V.P.T., Tri N.M., Hoang L.N., Tuan L.V., Ho Q.T., Le N.Q.K., & Kang J.H. (2022). Automatic Detection of Meniscus Tears Using Backbone Convolutional Neural Networks on Knee MRI. Journal of Magnetic Resonance Imaging, 57:740-749. https://doi.org/10.1002/jmri.28284
Published in Computational and Structural Biotechnology Journal, 2022
Recommended citation: Vo T.H., Nguyen N.T.K., Kha, Q.H., & Le N.Q.K. (2022). On the road to explainable AI in drug-drug interactions prediction: a systematic review. Computational and Structural Biotechnology Journal, 20, 2112-2123. https://doi.org/10.1016/j.csbj.2022.04.021
Published in Molecular Informatics, 2022
Recommended citation: Hung T.N.K., Le N.Q.K., Le N.H., Tuan L.V., Nguyen T.P., Thi C., & Kang J.H. (2022). An AI-based prediction model for drug-drug interactions in osteoporosis and Paget’s diseases from SMILES. Molecular Informatics, 41, 2100264. https://doi.org/10.1002/minf.202100264
Published in Journal of Proteome Research, 2021
Recommended citation: Tng S.S., Le N.Q.K., Yeh H.Y., & Chua M.C.H. (2022). Improved Prediction Model of Protein Lysine Crotonylation Sites Using Bidirectional Recurrent Neural Networks. Journal of Proteome Research, 21 (1), 265-273. https://doi.org/10.1021/acs.jproteome.1c00848
Published in Briefings in Bioinformatics, 2021
Recommended citation: Ho Q.T., Le N.Q.K., & Ou Y.Y. (2021). mCNN-ETC: Identifying electron transporters and their functional families by using multiple windows scanning techniques in convolutional neural networks with evolutionary information of protein sequences. Briefings in Bioinformatics, 23(1), bbab352. https://doi.org/10.1093/bib/bbab352
Published in Applied Intelligence, 2021
Recommended citation: Huynh T.T., Lin C.M., Le N.Q.K., Vu M.T., Nguyen N.P., & Chao F. (2021). Intelligent wavelet fuzzy brain emotional controller using dual function-link network for uncertain nonlinear control systems. Applied Intelligence, 52, 2720–2744. https://doi.org/10.1007/s10489-021-02482-4
Published in Computers in Biology and Medicine, 2021
Recommended citation: Le N.Q.K., Truong N.K.H., Do D.T., Luu H.T.L., Luong H.D., & Huynh T.T. (2021). Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI. Computers in Biology and Medicine, 132, 104320. https://doi.org/10.1016/j.compbiomed.2021.104320
Published in Briefings in Bioinformatics, 2021
Recommended citation: Le N.Q.K., Ho Q.T., Nguyen T.T.D., & Ou Y.Y. (2021). A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information. Briefings in Bioinformatics, 22(5), bbab005. https://doi.org/10.1093/bib/bbab005
Published in IEEE Transactions on Systems, Man and Cybernetics: Systems, 2020
Recommended citation: Huynh T.T., Lin C.M., Le T.L., Le N.Q.K., Vu V.P., & Chao F. (2022). Self-Organizing Double Function-Link Fuzzy Brain Emotional Control System Design for Uncertain Nonlinear Systems. IEEE Transactions on Systems, Man and Cybernetics: Systems, 52(3), 1852-1868. https://doi.org/10.1109/TSMC.2020.3036404
Published in Chemometrics and Intelligent Laboratory Systems, 2020
Recommended citation: Sua J.N., Lim S.Y., Yulius M.H., Su X., Yapp E.K.Y., Le N.Q.K., Yeh H.Y., & Chua M.C.H. (2020). Incorporating convolutional neural networks and sequence graph transform for identifying multilabel protein Lysine PTM sites. Chemometrics and Intelligent Laboratory Systems, 206, 104171. https://doi.org/10.1016/j.chemolab.2020.104171
Published in Briefings in Bioinformatics, 2020
Recommended citation: Do D.T., Le T.Q.T., & Le N.Q.K. (2020). Using deep neural networks and biological sub-words to detect protein S-sulfenylation sites. Briefings in Bioinformatics, 22(3), bbaa128. https://doi.org/10.1093/bib/bbaa128
Published in IEEE Transactions on Industrial Electronics, 2019
Recommended citation: Huynh T.T., Lin C.M., Le T.L., Cho H.Y., Pham T.T., Le N.Q.K., Chao F. (2020). A New Self-Organizing Fuzzy Cerebellar Model Articulation Controller for Uncertain Nonlinear Systems Using Overlapped Gaussian Membership Functions. IEEE Transactions on Industrial Electronics, vol. 67, no. 11, pp. 9671-9682. https://doi.org/10.1109/TIE.2019.2952790
Published in Neurocomputing, 2019
Recommended citation: Le N.Q.K., Ho Q.T., Yapp E.K.Y., Ou Y.Y., & Yeh H.Y. (2020). DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain’s complexes. Neurocomputing, 375, 71-79. https://doi.org/10.1016/j.neucom.2019.09.070
Published in IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019
Recommended citation: Le N.Q.K., & Nguyen B.P. (2021). Prediction of FMN Binding Sites in Electron Transport Chains based on 2-D CNN and PSSM Profiles. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(6), 2189-2197. https://doi.org/10.1109/TCBB.2019.2932416
Published in Journal of Proteome Research, 2019
Recommended citation: Le, N.Q.K. (2019). Fertility-GRU: Identifying Fertility-Related Proteins by Incorporating Deep-Gated Recurrent Units and Original Position-Specific Scoring Matrix Profiles. Journal of Proteome Research, 18(9), 3503-3511. https://doi.org/10.1021/acs.jproteome.9b00411
Published in Computer Methods and Programs in Biomedicine, 2019
Recommended citation: Le N.Q.K., Huynh T.T., Yapp E.K.Y., & Yeh H.Y. (2019). Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles. Computer Methods and Programs in Biomedicine, 177, 81-88. https://doi.org/10.1016/j.cmpb.2019.05.016
Published in Bioinformatics, 2018
Recommended citation: Taju S.W., Nguyen T.T.D., Le N.Q.K., Kusuma R.M.I., & Ou Y.Y. (2018). DeepEfflux: a 2D convolutional neural network model for identifying families of efflux proteins in transporters. Bioinformatics, 34(18), 3111-3117. https://doi.org/10.1093/bioinformatics/bty302
Published in Journal of Computational Chemistry, 2017
Recommended citation: Le N.Q.K., Ho Q.T., & Ou Y.Y. (2017). Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins. Journal of Computational Chemistry, 38(23), 2000-2006. https://doi.org/10.1002/jcc.24842