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

Prediction of FAD binding sites in electron transport proteins according to efficient radial basis function networks and significant amino acid pairs

Le N.Q.K. & Ou Y.Y. (2016). Prediction of FAD binding sites in electron transport proteins according to efficient radial basis function networks and significant amino acid pairs. BMC Bioinformatics, 17(1), 298.

Incorporating efficient radial basis function networks and significant amino acid pairs for predicting GTP binding sites in transport proteins

Le N.Q.K. & Ou Y.Y. (2016). Incorporating efficient radial basis function networks and significant amino acid pairs for predicting GTP binding sites in transport proteins. BMC Bioinformatics, 17(19), 183.

Identifying the molecular functions of electron transport proteins using radial basis function networks and biochemical properties

Le N.Q.K., Nguyen T.T.D., & Ou Y.Y. (2017). Identifying the molecular functions of electron transport proteins using radial basis function networks and biochemical properties. J Mol Graph Model, 73, 166-178.

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.

Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks

Le N.Q.K., Ho Q.T., & Ou Y.Y. (2018). Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks. Analytical Biochemistry, 555, 33-41.

Incorporating post translational modification information for enhancing the predictive performance of membrane transport proteins

Le N.Q.K., Sandag G.A., & Ou Y.Y. (2018). Incorporating post translational modification information for enhancing the predictive performance of membrane transport proteins. Computational Biology and Chemistry, 77, 251-260.

SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data

Le N.Q.K., & Nguyen V.N. (2019). SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data. PeerJ Computer Science, 5:e177.

iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou’s 5-step rule and word embedding

Le N.Q.K., Yapp E.K.Y., Ho Q.T., Nagasundaram N., Ou Y.Y., & Yeh H.Y. (2019). iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou’s 5-step rule and word embedding. Analytical Biochemistry, 571, 53-61.

Using two-dimensional convolutional neural networks for identifying GTP binding sites in Rab proteins

Le N.Q.K., Ho Q.T., & Ou Y.Y. (2019). Using two-dimensional convolutional neural networks for identifying GTP binding sites in Rab proteins. Journal of Bioinformatics and Computational Biology, 17(1), 1950005.

iMotor-CNN: Identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou’s 5-step rule

Le N.Q.K., Yapp E.K.Y., Ou Y.Y., & Yeh H.Y. (2019). iMotor-CNN: Identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou’s 5-step rule. Analytical Biochemistry, 575, 17-26.

iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou’s 5-step rule

Le N.Q.K. (2019). iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou’s 5-step rule. Molecular Genetics and Genomics, 294(5), 1173-1182.

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.

ET-GRU: using multi-layer gated recurrent units to identify electron transport proteins

Le N.Q.K., Yapp E.K.Y., & Yeh H.Y. (2019). ET-GRU: using multi-layer gated recurrent units to identify electron transport proteins. BMC Bioinformatics, 20(1), 377.

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.

A sequence-based approach for identifying recombination spots in Saccharomyces cerevisiae by using hyper-parameter optimization in FastText and support vector machine

Do D.T., & Le N.Q.K. (2019). A sequence-based approach for identifying recombination spots in Saccharomyces cerevisiae by using hyper-parameter optimization in FastText and support vector machine. Chemometrics and Intelligent Laboratory Systems, 194, 103855.

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.

Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture

Le N.Q.K., Yapp E.K.Y., Nagasundaram N., Chua M.C.H., & Yeh H.Y. (2019). Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture. Computational and Structural Biotechnology Journal, 17, 1245-1254.

Classifying Promoters by Interpreting the Hidden Information of DNA Sequences via Deep Learning and Combination of Continuous FastText N-Grams

Le N.Q.K., Yapp E.K.Y., Nagasundaram N., & Yeh H.Y. (2019). Classifying Promoters by Interpreting the Hidden Information of DNA Sequences via Deep Learning and Combination of Continuous FastText N-Grams. Frontiers in Bioengineering and Biotechnology, 7:305.

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.

Identifying SNAREs by Incorporating Deep Learning Architecture and Amino Acid Embedding Representation

Le N.Q.K., & Huynh T.T. (2019). Identifying SNAREs by Incorporating Deep Learning Architecture and Amino Acid Embedding Representation. Frontiers in Physiology, 10:1501.

Classification of adaptor proteins using recurrent neural networks and PSSM profiles

Le N.Q.K., Nguyen Q.H., Chen X., Rahardja S., & Nguyen B.P. (2019). Classification of adaptor proteins using recurrent neural networks and PSSM profiles. BMC Genomics, 20, 966.

Using extreme gradient boosting to identify origin of replication in Saccharomyces cerevisiae via hybrid features

Do D.T. & Le N.Q.K. (2020). Using extreme gradient boosting to identify origin of replication in Saccharomyces cerevisiae via hybrid features. Genomics, 112(3), 2445-2451.

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.

Use Chou’s 5-steps rule with different word embedding types to boost performance of electron transport protein prediction model

Nguyen T.T.D., Ho Q.T., Le N.Q.K., Phan D.V., & Ou Y.Y. (2022). Use Chou’s 5-steps rule with different word embedding types to boost performance of electron transport protein prediction model. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(2), 1235-1244.

XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma

Le N.Q.K., Do T.D., Chiu F.Y., Yapp E.K.Y., Yeh H.Y., & Chen C.Y. (2020). XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma. Journal of Personalized Medicine, 10(3), 128.

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.

Machine Learning Model for Identifying Antioxidant Proteins Using Features Calculated from Primary Sequences

Luu H.T.L., Le N.H., Le V.T., Ho T.B., Truong N.K.H., Nguyen N.T.K., Luong H.D., & Le N.Q.K. (2020). Machine Learning Model for Identifying Antioxidant Proteins Using Features Calculated from Primary Sequences. Biology, 9(10), 325.

A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification

Le N.Q.K., Do D.T., Truong N.K.H., Huynh T.T., Luu H.T.L., & Nguyen N.T.K. (2020). A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification. International Journal of Molecular Sciences, 21(23), 9070.

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.

A sequence-based prediction of Kruppel-like factors proteins using XGBoost and optimized features

Le N.Q.K., Do D.T., Nguyen T.T.D., & Le Q.A. (2021). A sequence-based prediction of Kruppel-like factors proteins using XGBoost and optimized features. Gene, 787, 145643.

An extensive examination of discovering 5-Methylcytosine Sites in Genome-Wide DNA Promoters using machine learning based approaches

Nguyen T.T.D., Tran T.A., Le N.Q.K., Pham D.M., & Ou Y.Y. (2022). An extensive examination of discovering 5-Methylcytosine Sites in Genome-Wide DNA Promoters using machine learning based approaches. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(1), 87-94.

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.

Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer

Le V.H., Kha Q.H., Truong N.K.H., & Le N.Q.K. (2021). Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer. Cancers, 13(14), 3616.

Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer

Le N.Q.K., Kha Q.H., Nguyen V.H., Chen Y.C,, Cheng S.J., Chen C.Y. (2021). Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer. International Journal of Molecular Sciences, 22(17), 9254.

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.

Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis

Dang H.H., Ta H.D.K., Nguyen T.T.T., Anuraga G., Wang C.Y., Lee K.H., & Le N.Q.K. (2021). Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis. Biomedicines, 9(9), 1144.

Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas

Kha Q.H., Le V.H., Hung T.N.K., & Le N.Q.K. (2021). Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas. Cancers, 13(21), 5398.

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.

Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes

Le N.Q.K. & Ho Q.T. (2022). Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes. Methods, 204, 199-206.

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.

Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches

Huynh Q.T.V., Le N.Q.K., Huang S.Y., Ho B.T., Vu T.H., Pham H.T.M., Pham A.L., Hou J.W., Nguyen N.T.K., & Chen Y.C. (2022). Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches. PLOS ONE, 17(1): e0261965.

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.

4-D Memristive Chaotic Systems-Based Audio Secure Communication Using Dual-Function-Link Fuzzy Brain Emotional Controller

Huynh T.T., Lin C.M., Nguyen N.P., Le N.Q.K., Vu M.T., Pham D.H., Vu V.P., & Chao F. (2022). 4-D Memristive Chaotic Systems-Based Audio Secure Communication Using Dual-Function-Link Fuzzy Brain Emotional Controller. International Journal of Fuzzy Systems, 24, 2946–2968.

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.

BERT-Promoter: an improved sequence-based predictor of DNA promoter using BERT pre-trained model and SHAP feature selection

Le N.Q.K., Ho Q.T., Nguyen V.N., & Chang J.S. (2022). BERT-Promoter: an improved sequence-based predictor of DNA promoter using BERT pre-trained model and SHAP feature selection. Computational Biology and Chemistry, 99, 107732.

A radiomics-based machine learning model for prediction of tumor mutational burden in lower-grade gliomas

Lam L.H.T., Chu N.T., Tran T.O., Do D.T., & Le N.Q.K. (2022). A radiomics-based machine learning model for prediction of tumor mutational burden in lower-grade gliomas. Cancers, 14(14), 3492.

Prospective role and immunotherapeutic targets of sideroflexin protein family in lung adenocarcinoma: evidence from bioinformatics validation

Dang H.H., Ta H.D.K., Nguyen T.T.T., Anuraga G., Wang C.Y., Lee K.H., & Le N.Q.K. (2022). Prospective role and immunotherapeutic targets of sideroflexin protein family in lung adenocarcinoma: evidence from bioinformatics validation. Functional & Integrative Genomics, 22, 1057–1072.

Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach

Do D.T., Yang M.R., Lam L.H.T., Le N.Q.K., & Wu Y.W. (2022). Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach. Scientific Reports, 12, 13412.

An interpretable deep learning model for classifying adaptor protein complexes from sequence information

Kha Q.H., Tran T.O., Nguyen T.T.D., Nguyen V.N., Than K., & Le N.Q.K. (2022). An interpretable deep learning model for classifying adaptor protein complexes from sequence information. Methods, 207, 90-96.

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.

Improved Prediction Model of Protein and Peptide Toxicity by Integrating Channel Attention into a Convolutional Neural Network and Gated Recurrent Units

Zhao Z., Gui J., Yao A., Le N.Q.K., & Chua M.C.H. (2022). Improved Prediction Model of Protein and Peptide Toxicity by Integrating Channel Attention into a Convolutional Neural Network and Gated Recurrent Units. ACS Omega, 7(44), 40569–40577.

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.

Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug–Food Interactions from Chemical Structures

Kha Q.H., Le V.H., Hung T.N.K., Nguyen N.T.K., & Le N.Q.K.. (2023). Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug–Food Interactions from Chemical Structures. Sensors, 23(8), 3962.

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.

Recent progress in machine learning approaches for predicting carcinogenicity in drug development

Le N.Q.K., Tran T.X., Nguyen P.A., Ho T.T., & Nguyen V.N. (2024). Recent progress in machine learning approaches for predicting carcinogenicity in drug development. Expert Opinion on Drug Metabolism & Toxicology, 20(7), 621–628.

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., Huynh P.K., 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. (2024). Deep Learning-based Integrated System for Intraoperative Blood Loss Quantification in Surgical Sponges. IEEE Journal of Biomedical and Health Informatics.