Selected publications
Full publication list at (Google Scholar Profile)
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 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
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
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 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
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 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
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 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
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
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
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
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
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 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
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 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
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 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
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 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
Published in Biomedicines, 2021
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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
Published in Briefings in Bioinformatics, 2021
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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 International Journal of Molecular Sciences, 2021
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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
Published in Cancers, 2021
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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
Published in Applied Intelligence, 2021
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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 IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021
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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
Published in Computers in Biology and Medicine, 2021
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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
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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 International Journal of Molecular Sciences, 2020
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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
Published in Biology, 2020
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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
Published in Chemometrics and Intelligent Laboratory Systems, 2020
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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 Journal of Personalized Medicine, 2020
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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
Published in IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020
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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
Published in Briefings in Bioinformatics, 2020
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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 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
Published in IEEE Transactions on Industrial Electronics, 2019
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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 Frontiers in Bioengineering and Biotechnology, 2019
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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
Published in Computational and Structural Biotechnology Journal, 2019
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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
Published in Neurocomputing, 2019
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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 Chemometrics and Intelligent Laboratory Systems, 2019
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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
Published in IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019
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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
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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
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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 Molecular Genetics and Genomics, 2019
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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
Published in Analytical Biochemistry, 2019
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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
Published in Journal of Bioinformatics and Computational Biology, 2019
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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
Published in Analytical Biochemistry, 2019
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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
Published in PeerJ Computer Science, 2019
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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
Published in Computational Biology and Chemistry, 2018
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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
Published in Analytical Biochemistry, 2018
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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
Published in Bioinformatics, 2018
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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
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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
Published in Journal of Molecular Graphics and Modelling, 2017
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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
Published in BMC Bioinformatics, 2016
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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
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