CV

Education

Ph.D. in Computer Science & Engineering, Yuan Ze University, Taiwan, 2018
M.S. in Computer Science & Engineering, Yuan Ze University, Taiwan, 2014
B.Eng. in Information Technology, University of Dalat, 2010

Work experience

February 2023 - present: Associate Professor
- Taipei Medical University (TMU), Taiwan
- College of Medicine
- Professional Master Program in Artificial Intelligence in Medicine
August 2019 - February 2023: Assistant Professor
- Taipei Medical University (TMU), Taiwan
- College of Medicine
- Professional Master Program in Artificial Intelligence in Medicine
August 2018 - August 2019: Research Fellow
- Nanyang Technological University (NTU), Singapore
- School of Humanities
- Supervisor: Assistant Professor Hui-Yuan Yeh
September 2012 - July 2018: Research Assistant & Teaching Assistant
- Yuan Ze University (YZU), Taiwan
- Department of Computer Science & Engineering
- Supervisor: Associate Professor Yu-Yen Ou

Research Interests

Explainable Artificial Intelligence
- Machine Learning & Deep Learning
- Natural Language Processing
Biomedical & Healthcare Informatics
- Bioinformatics
- Genomics
- Radiomics
Multidisciplinary

Publications

Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins

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.

DeepEfflux: a 2D convolutional neural network model for identifying families of efflux proteins in transporters

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.

Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles

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.

Fertility-GRU: Identifying Fertility-Related Proteins by Incorporating Deep-Gated Recurrent Units and Original Position-Specific Scoring Matrix Profiles

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.

Prediction of FMN Binding Sites in Electron Transport Chains based on 2-D CNN and PSSM Profiles

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.

DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain’s complexes

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.

A New Self-Organizing Fuzzy Cerebellar Model Articulation Controller for Uncertain Nonlinear Systems Using Overlapped Gaussian Membership Functions

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.

Using deep neural networks and biological sub-words to detect protein S-sulfenylation sites

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.

Incorporating convolutional neural networks and sequence graph transform for identifying multilabel protein Lysine PTM sites

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.

Self-Organizing Double Function-Link Fuzzy Brain Emotional Control System Design for Uncertain Nonlinear Systems

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.

A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information

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.

Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI

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.

Intelligent wavelet fuzzy brain emotional controller using dual function-link network for uncertain nonlinear control systems

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.

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

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.

Improved Prediction Model of Protein Lysine Crotonylation Sites Using Bidirectional Recurrent Neural Networks

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.

An AI-based prediction model for drug-drug interactions in osteoporosis and Paget’s diseases from SMILES

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.

On the road to explainable AI in drug-drug interactions prediction: a systematic review

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.

Automatic Detection of Meniscus Tears Using Backbone Convolutional Neural Networks on Knee MRI

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.

Molecular subtype classification of low-grade gliomas using magnetic resonance imaging-based radiomics and machine learning

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.

Identifying SNARE Proteins Using Alignment-Free Method Based on Multi-Scan Convolutional Neural Network and PSSM Profiles

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.

MaskDNA-PGD: an innovative deep learning model for detecting DNA methylation by integrating mask sequences and adversarial PGD training as a data augmentation method

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.

Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding

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.

Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer

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.

ALDH2 as a potential stem cell-related biomarker in lung adenocarcinoma: Comprehensive multi-omics analysis

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.

Predicting EGFR Mutation Status in Non–Small Cell Lung Cancer Using Artificial Intelligence: A Systematic Review and Meta-Analysis

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.

Diffusion-tensor imaging and dynamic susceptibility contrast MRIs improve radiomics-based machine learning model of MGMT promoter methylation status in glioblastomas

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.

Leveraging transformers-based language models in proteome bioinformatics

Le N.Q.K.. (2023). Leveraging transformers-based language models in proteome bioinformatics. PROTEOMICS, 2300011.

Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection

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.

VF-Pred: Predicting virulence factor using sequence alignment percentage and ensemble learning models

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.

Predicting emerging drug interactions using GNNs

Le N.Q.K. (2023). Predicting emerging drug interactions using GNNs. Nature Computational Science, 3, 1007–1008.

Hematoma expansion prediction: still navigating the intersection of deep learning and radiomics

Le N.Q.K. (2024). Hematoma expansion prediction: still navigating the intersection of deep learning and radiomics. European Radiology, 34, 2905–2907.

Multi-Class Deep Learning Model for Detecting Pediatric Distal Forearm Fractures Based on the AO/OTA Classification

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.

Sa-TTCA: An SVM-based approach for tumor T-cell antigen classification using features extracted from biological sequencing and natural language processing

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.

Enhancing Nasopharyngeal Carcinoma Survival Prediction: Integrating Pre- and Post-Treatment MRI Radiomics with Clinical Data

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.

Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation

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.

Deep Learning-based Integrated System for Intraoperative Blood Loss Quantification in Surgical Sponges

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.

Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images

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.

Unlocking RNA mysteries: Predicting subcellular localizations with AI

Le N.Q.K. (2025). Unlocking RNA mysteries: Predicting subcellular localizations with AI. Molecular Therapy Nucleic Acids, 36(1), 102481.

MLG2Net: Molecular Global Graph Network for Drug Response Prediction in Lung Cancer Cell Lines

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.

Artificial intelligence in predicting EGFR mutations from whole slide images in lung Cancer: A systematic review and Meta-Analysis

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.

Evaluating YOLO for dental caries diagnosis: a systematic review and meta-analysis

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.

AI-driven multi-modal framework for prognostic modeling in glioblastoma: Enhancing clinical decision support

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.

OASIS-Net: An Obstetric Adversarial Semi-Supervised Image Segmentation Network for Cervical and Fetal Head Ultrasound Imaging

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.

Translational deep learning models for risk stratification to predict prognosis and immunotherapy response in gastric cancer using digital pathology

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.

EPINTLM: enhancer–promoter prediction with pretrained k-mer embeddings and residual cross-attention

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.

A CT radiomics signature enables risk stratification and survival prediction in colorectal liver metastases

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.