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Prediction of FAD binding sites in electron transport proteins according to efficient radial basis function networks and significant amino acid pairs
Published in BMC Bioinformatics, 2016
Recommended citation: 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. https://doi.org/10.1186/s12859-016-1163-x
Incorporating efficient radial basis function networks and significant amino acid pairs for predicting GTP binding sites in transport proteins
Published in BMC Bioinformatics, 2016
Recommended citation: 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. https://doi.org/10.1186/s12859-016-1369-y
Identifying the molecular functions of electron transport proteins using radial basis function networks and biochemical properties
Published in Journal of Molecular Graphics and Modelling, 2017
Recommended citation: 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. https://doi.org/10.1016/j.jmgm.2017.01.003
Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins
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
DeepEfflux: a 2D convolutional neural network model for identifying families of efflux proteins in transporters
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
Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks
Published in Analytical Biochemistry, 2018
Recommended citation: 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. https://doi.org/10.1016/j.ab.2018.06.011
Incorporating post translational modification information for enhancing the predictive performance of membrane transport proteins
Published in Computational Biology and Chemistry, 2018
Recommended citation: 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. https://doi.org/10.1016/j.compbiolchem.2018.10.010
SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data
Published in PeerJ Computer Science, 2019
Recommended citation: 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. https://doi.org/10.7717/peerj-cs.177
iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou’s 5-step rule and word embedding
Published in Analytical Biochemistry, 2019
Recommended citation: 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. https://doi.org/10.1016/j.ab.2019.02.017
Using two-dimensional convolutional neural networks for identifying GTP binding sites in Rab proteins
Published in Journal of Bioinformatics and Computational Biology, 2019
Recommended citation: 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. https://doi.org/10.1142/S0219720019500057
iMotor-CNN: Identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou’s 5-step rule
Published in Analytical Biochemistry, 2019
Recommended citation: 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. https://doi.org/10.1016/j.ab.2019.03.017
iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou’s 5-step rule
Published in Molecular Genetics and Genomics, 2019
Recommended citation: 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. https://doi.org/10.1007/s00438-019-01570-y
Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles
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
ET-GRU: using multi-layer gated recurrent units to identify electron transport proteins
Published in BMC Bioinformatics, 2019
Recommended citation: 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. https://doi.org/10.1186/s12859-019-2972-5
Fertility-GRU: Identifying Fertility-Related Proteins by Incorporating Deep-Gated Recurrent Units and Original Position-Specific Scoring Matrix Profiles
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
Prediction of FMN Binding Sites in Electron Transport Chains based on 2-D CNN and PSSM Profiles
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
A sequence-based approach for identifying recombination spots in Saccharomyces cerevisiae by using hyper-parameter optimization in FastText and support vector machine
Published in Chemometrics and Intelligent Laboratory Systems, 2019
Recommended citation: 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. https://doi.org/10.1016/j.chemolab.2019.103855
DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain’s complexes
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
Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture
Published in Computational and Structural Biotechnology Journal, 2019
Recommended citation: 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. https://doi.org/10.1016/j.csbj.2019.09.005
Classifying Promoters by Interpreting the Hidden Information of DNA Sequences via Deep Learning and Combination of Continuous FastText N-Grams
Published in Frontiers in Bioengineering and Biotechnology, 2019
Recommended citation: 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. https://doi.org/10.3389/fbioe.2019.00305
A New Self-Organizing Fuzzy Cerebellar Model Articulation Controller for Uncertain Nonlinear Systems Using Overlapped Gaussian Membership Functions
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
Identifying SNAREs by Incorporating Deep Learning Architecture and Amino Acid Embedding Representation
Published in Frontiers in Physiology, 2019
Recommended citation: 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. https://doi.org/10.3389/fphys.2019.01501
Classification of adaptor proteins using recurrent neural networks and PSSM profiles
Published in BMC Genomics, 2019
Recommended citation: 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. https://doi.org/10.1186/s12864-019-6335-4
Using extreme gradient boosting to identify origin of replication in Saccharomyces cerevisiae via hybrid features
Published in Genomics, 2020
Recommended citation: 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. https://doi.org/10.1016/j.ygeno.2020.01.017
Using deep neural networks and biological sub-words to detect protein S-sulfenylation sites
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
Use Chou’s 5-steps rule with different word embedding types to boost performance of electron transport protein prediction model
Published in IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020
Recommended citation: 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. https://doi.org/10.1109/TCBB.2020.3010975
XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma
Published in Journal of Personalized Medicine, 2020
Recommended citation: 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. https://doi.org/10.3390/jpm10030128
Incorporating convolutional neural networks and sequence graph transform for identifying multilabel protein Lysine PTM sites
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
Machine Learning Model for Identifying Antioxidant Proteins Using Features Calculated from Primary Sequences
Published in Biology, 2020
Recommended citation: 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. https://doi.org/10.3390/biology9100325
A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification
Published in International Journal of Molecular Sciences, 2020
Recommended citation: 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. https://doi.org/10.3390/ijms21239070
Self-Organizing Double Function-Link Fuzzy Brain Emotional Control System Design for Uncertain Nonlinear Systems
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
A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information
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
Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI
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
A sequence-based prediction of Kruppel-like factors proteins using XGBoost and optimized features
Published in Gene, 2021
Recommended citation: 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. https://doi.org/10.1016/j.gene.2021.145643
An extensive examination of discovering 5-Methylcytosine Sites in Genome-Wide DNA Promoters using machine learning based approaches
Published in IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021
Recommended citation: 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. https://doi.org/10.1109/TCBB.2021.3082184
Intelligent wavelet fuzzy brain emotional controller using dual function-link network for uncertain nonlinear control systems
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
Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer
Published in Cancers, 2021
Recommended citation: 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. https://doi.org/10.3390/cancers13143616
Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer
Published in International Journal of Molecular Sciences, 2021
Recommended citation: 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. https://doi.org/10.3390/ijms22179254
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
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
Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis
Published in Biomedicines, 2021
Recommended citation: 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. https://doi.org/10.3390/biomedicines9091144
Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas
Published in Cancers, 2021
Recommended citation: 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. https://doi.org/10.3390/cancers13215398
Improved Prediction Model of Protein Lysine Crotonylation Sites Using Bidirectional Recurrent Neural Networks
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
Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes
Published in Methods, 2021
Recommended citation: 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. https://doi.org/10.1016/j.ymeth.2021.12.004
An AI-based prediction model for drug-drug interactions in osteoporosis and Paget’s diseases from SMILES
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
Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches
Published in PLOS ONE, 2022
Recommended citation: 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. https://doi.org/10.1371/journal.pone.0261965
On the road to explainable AI in drug-drug interactions prediction: a systematic review
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
4-D Memristive Chaotic Systems-Based Audio Secure Communication Using Dual-Function-Link Fuzzy Brain Emotional Controller
Published in International Journal of Fuzzy Systems, 2022
Recommended citation: 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. https://doi.org/10.1007/s40815-022-01312-0
Automatic Detection of Meniscus Tears Using Backbone Convolutional Neural Networks on Knee MRI
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
Molecular subtype classification of low-grade gliomas using magnetic resonance imaging-based radiomics and machine learning
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
BERT-Promoter: an improved sequence-based predictor of DNA promoter using BERT pre-trained model and SHAP feature selection
Published in Computational Biology and Chemistry, 2022
Recommended citation: 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. https://doi.org/10.1016/j.compbiolchem.2022.107732
A radiomics-based machine learning model for prediction of tumor mutational burden in lower-grade gliomas
Published in Cancers, 2022
Recommended citation: 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. https://doi.org/10.3390/cancers14143492
Prospective role and immunotherapeutic targets of sideroflexin protein family in lung adenocarcinoma: evidence from bioinformatics validation
Published in Functional & Integrative Genomics, 2022
Recommended citation: 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. https://doi.org/10.1007/s10142-022-00883-3
Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach
Published in Scientific Reports, 2022
Recommended citation: 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. https://doi.org/10.1038/s41598-022-17707-w
An interpretable deep learning model for classifying adaptor protein complexes from sequence information
Published in Methods, 2022
Recommended citation: 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. https://doi.org/10.1016/j.ymeth.2022.09.007
Identifying SNARE Proteins Using Alignment-Free Method Based on Multi-Scan Convolutional Neural Network and PSSM Profiles
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
Improved Prediction Model of Protein and Peptide Toxicity by Integrating Channel Attention into a Convolutional Neural Network and Gated Recurrent Units
Published in ACS Omega, 2022
Recommended citation: 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. https://doi.org/10.1021/acsomega.2c05881
MaskDNA-PGD: an innovative deep learning model for detecting DNA methylation by integrating mask sequences and adversarial PGD training as a data augmentation method
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
Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding
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
Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer
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
ALDH2 as a potential stem cell-related biomarker in lung adenocarcinoma: Comprehensive multi-omics analysis
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
Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug–Food Interactions from Chemical Structures
Published in Sensors, 2023
Recommended citation: 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. https://doi.org/10.3390/s23083962
Predicting EGFR Mutation Status in Non–Small Cell Lung Cancer Using Artificial Intelligence: A Systematic Review and Meta-Analysis
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
Diffusion-tensor imaging and dynamic susceptibility contrast MRIs improve radiomics-based machine learning model of MGMT promoter methylation status in glioblastomas
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
Leveraging transformers-based language models in proteome bioinformatics
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
Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection
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
VF-Pred: Predicting virulence factor using sequence alignment percentage and ensemble learning models
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
Predicting emerging drug interactions using GNNs
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
Hematoma expansion prediction: still navigating the intersection of deep learning and radiomics
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
Multi-Class Deep Learning Model for Detecting Pediatric Distal Forearm Fractures Based on the AO/OTA Classification
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
Sa-TTCA: An SVM-based approach for tumor T-cell antigen classification using features extracted from biological sequencing and natural language processing
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
Enhancing Nasopharyngeal Carcinoma Survival Prediction: Integrating Pre- and Post-Treatment MRI Radiomics with Clinical Data
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. https://doi.org/10.1007/s10278-024-01109-7
Recent progress in machine learning approaches for predicting carcinogenicity in drug development
Published in Expert Opinion on Drug Metabolism & Toxicology, 2024
Recommended citation: 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. https://doi.org/10.1080/17425255.2024.2356162
Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation
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
Deep Learning-based Integrated System for Intraoperative Blood Loss Quantification in Surgical Sponges
Published in IEEE Journal of Biomedical and Health Informatics, 2024
Recommended citation: 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. https://doi.org/10.1109/JBHI.2024.3499852
teaching
Neural Networks and Application on Bioinformatics
Undergraduate course, TMU College of Interdisciplinary Studies (iCollege), 2020
This course will introduce the fundamentals of different neural networks and their applications. Students can learn how to implement different types of neural networks using Python programming on Keras+Tensorflow platform. Upon completion of the course, students have extensive skills with neural networks and especially their applications in bioinformatics.
Application of Artificial Intelligence in Genomics
Graduate course, TMU Professional Master Program in Artificial Intelligence in Medicine, 2020
Artificial intelligence (AI) proves to have enormous potential in many areas of health, including biomedical data analysis and drug discovery. Genomics is also a field that attracts a lot of AI experts nowadays. AI could be applied in genomics in representing and predicting the information of genomics such as DNA, RNA, or protein sequence. This course focuses on this aspect, to show how to apply AI in genomics from DNA, RNA, or protein level.
Computer programming & data processing
Graduate course, TMU Professional Master Program in Artificial Intelligence in Medicine, 2020
Nowadays, Python has been increasingly become one of the top programming languages that can be used in a variety of fields. Especially in medical data analysis, it is the most popular language. Via Python, many tasks from data analysis and machine learning, deep learning have been resolved efficiently. This course will introduce Python programming language focusing mostly on data analysis and machine learning, especially how to apply it into medical data.
Seminar
Graduate course, TMU Professional Master Program in Artificial Intelligence in Medicine, 2020
The course is for graduate students to learn and practice their skills of academic research and presentation.
Python Programming in Medical Data Analysis
Undergraduate course, TMU College of Interdisciplinary Studies (iCollege), 2021
Nowadays, Python has been increasingly become one of the top programming languages that can be used in a variety of fields. Especially in medical field, many tasks from data analysis and machine learning, deep learning have been resolved efficiently by using Python. This course will introduce basic ideas ofPython programming language and apply it to medical data i.e., electronic health records, medical imaging.
Artificial Intelligence in Bioinformatics
MOOCs course, FutureLearn, 2021
Artificial intelligence (AI) is transforming the field of bioinformatics. On this course, you’ll learn the basics of collecting, analysing, and modeling bioinformatics data using AI. You’ll find out how to collect and explore bioinformatics data from public resources and then use AI to analyse and model this data in order to better understand key biological processes.
Basic computer programing
Graduate course, TMU Professional Master Program in Artificial Intelligence in Medicine, 2021
Nowadays, Python has been increasingly become one of the top programming languages that can be used in a variety of fields. Especially in medical field, many tasks from data analysis and machine learning, deep learning have been resolved efficiently by using Python. This course will introduce basic ideas of Python programming language from data structures, functions, object-orient programming, regular expression, as well as graphic interface.
Advanced computer programing
Graduate course, TMU Professional Master Program in Artificial Intelligence in Medicine, 2022
Nowadays, Python has been increasingly become one of the top programming languages that can be used in a variety of fields. Especially in medical field, many tasks from data analysis and machine learning, deep learning have been resolved efficiently by using Python. This course will introduce Python programming language focusing mostly on data analysis and machine learning, especially how to apply it into medical data.
Basic programming
Undergraduate course, TMU Section of Liberal Arts, 2024
To provide medical students with a fundamental understanding of programming concepts and skills necessary for data analysis, automation, and problem-solving in healthcare settings. By the end of the course, students will be able to write and comprehend basic programs, apply algorithmic thinking to medical scenarios, and utilize programming techniques to enhance efficiency and decision-making in healthcare practice.