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Pages

Posts

Future Blog Post

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

member

portfolio

publications

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

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

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

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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

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

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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

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

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

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

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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

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

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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

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

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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

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

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

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

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

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

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

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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

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

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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

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

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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

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

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

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

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

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

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

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.