| 1. |
2024
Nguyen VN,Ho TT,Doan TD,Le NQK.
Using a hybrid neural network architecture for DNA sequence representation: A study on N4-methylcytosine sites
. Computers in Biology and Medicine
.2024
;(178):108664
|
|
| 2. |
2024
Tran TO,Le NQK.
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
.2024
;(174):108408
|
|
| 3. |
2024
Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation
. Journal of Assisted Reproduction and Genetics
.2024
;(41):2349-2358
|
|
| 4. |
2024
Enhancing Arabidopsis thaliana ubiquitination site prediction through knowledge distillation and natural language processing
. Methods
.2024
;(232):65-71
|
|
| 5. |
2024
Development and validation of a machine learning model for predicting drug-drug interactions with oral diabetes medications
. Methods
.2024
;(232):81-88
|
|
| 6. |
2024
Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine
. Journal of Assisted Reproduction and Genetics
.2024
;(41):239-252
|
|
| 7. |
2023
Singh S,Le NQK,Wang C.
VF-Pred: Predicting virulence factor using sequence alignment percentage and ensemble learning models
. Computers in Biology and Medicine
.2023
;(168):107662
|
|
| 8. |
2023
Zheng Z,Le NQK,Chua MCH.
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
.2023
;(232):104715
|
|
| 9. |
2023
Yuan Y,Chen K,Yu Y,Le NQK,Chua MCH.
Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding
. Briefings in Bioinformatics
.2023
;(24):1-10
|
|
| 10. |
2023
Le VH,Kha QH,Minh TNT,Nguyen VH,Le VL,Le NQK.
Development and validation of CT-based radiomics signature for overall survival prediction in multi-organ cancer
. Journal of Digital Imaging
.2023
;(36):911-922
|
|
| 11. |
2023
Tran TO,Vo TH,Lam LHT,Le NQK.
ALDH2 as a potential stem cell-related biomarker in lung adenocarcinoma: Comprehensive multi-omics analysis
. Computational and Structural Biotechnology Journal
.2023
;(21):1921-1929
|
|
| 12. |
2023
Huynh QTV,Minh TTT,Doan KK,Ho BT,Shen SC,Trinh TH, Vo TH, Le NQK, Nguyen NTK.
The Distribution of Autoantibodies by Age Group in Children with Type 1 Diabetes versus Type 2 Diabetes in Southern Vietnam
. Journal of Clinical Medicine
.2023
;(12):1420
|
|
| 13. |
2023
Kha QH,Le VH,Hung TNK,Nguyen NTK,Le NQK.
Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug–Food Interactions from Chemical Structures
. Sensors
.2023
;(23):3962
|
|
| 14. |
2023
Nguyen HS,Ho DKN,Nguyen NN,Tran HM,Tam KW,Le NQK.
Predicting EGFR Mutation Status in Non–Small Cell Lung Cancer Using Artificial Intelligence: A Systematic Review and Meta-Analysis
. Academic Radiology
.2023
|
|
| 15. |
2023
Minh TNT,Le VH,Le NQK.
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
.2023
;(86):105122
|
|
| 16. |
2023
Le NQK.
Leveraging transformers-based language models in proteome bioinformatics
. Proteomics
.2023
|
|
| 17. |
2023
Le VH,Minh TNT,Kha QH,Le NQK.
A transfer learning approach on MRI-based radiomics signature for overall survival prediction of low-grade and high-grade gliomas
. Medical & Biological Engineering & Computing
.2023
;(61):2699-2712
|
|
| 18. |
2023
Tran TO,Lam LHT,Le NQK.
Hyper-methylation of ABCG1 as an epigenetics biomarker in non-small cell lung cancer
. Functional & Integrative Genomics
.2023
;(23)
|
|
| 19. |
2023
Dang HH,Ta HDK,Nguyen TTT,Wang CY,Lee KH,Le NQK.
Identification of a Novel Eight-Gene Risk Model for Predicting Survival in Glioblastoma: A Comprehensive Bioinformatic Analysis
. Cancers
.2023
;(15)
|
|
| 20. |
2023
Le NQK,Li W,Cao Y.
Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection
. Briefings in Bioinformatics
.2023
;(24)
|
|
| 21. |
2023
Le NQK,Xu L.
Optimizing Hyperparameter Tuning in Machine Learning to Improve the Predictive Performance of Cross-Species N6-Methyladenosine Sites
. ACS Omega
.2023
;(8):39420-39426
|
|
| 22. |
2023
Tran TO,Vo TH,Le NQK.
Omics-based deep learning approaches for lung cancer decision-making and therapeutics development
. Briefings in Functional Genomics
.2023
|
|
| 23. |
2022
Le NQK,Ho QT.
Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes
. Methods
.2022
;(204):199-226
|
|
| 24. |
2022
Hung TNK,Le NQK,Le NH,Tuan LV,Nguyen TP,Thi C, Kang JH.
An AI-based prediction model for drug-drug interactions in osteoporosis and Paget's diseases from SMILES
. Molecular Informatics
.2022
;(41):2100264
|
|
| 25. |
2022
Huynh QTV,Le NQK,Huang SY,Ho BT,Vu TH,Pham HTM, Pham AL, Hou JW, Nguyen NTK, Chen YC.
Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches
. PLOS ONE
.2022
;(17):e0261965
|
|
| 26. |
2022
Vo TH,Nguyen NTK,Kha QH,Le NQK.
On the road to explainable AI in drug-drug interactions prediction: a systematic review
. Computational and Structural Biotechnology Journal
.2022
;(20):2112-2123
|
|
| 27. |
2022
Huynh TT,Lin CM,Pham DH,Nguyen NP,Le NQK,Vu MT, Vu VP, Chao F.
4-D Memristive Chaotic Systems-Based Audio Secure Communication Using Dual-Function-Link Fuzzy Brain Emotional Controller
. International Journal of Fuzzy Systems
.2022
;(24):pages2946-2968
|
|
| 28. |
2022
Vy VPT,Yao MMS,Le NQK,Chan WP.
Machine Learning Algorithm for Distinguishing Ductal Carcinoma In Situ from Invasive Breast Cancer
. Cancers
.2022
;(14):2437
|
|
| 29. |
2022
Hung TNK,Vy VPT,Tri NM,Hoang LN,Tuan LV,Ho QT, Le NQK, Kang JH.
Automatic Detection of Meniscus Tears Using Backbone Convolutional Neural Networks on Knee MRI
. Journal of Magnetic Resonance Imaging
.2022
|
|
| 30. |
2022
Lam LHT,Do DT,Diep DTN,Nguyet DLN,Truong QD,Tri TT, Thanh HN, Le NQK.
Molecular subtypes classification of low-grade gliomas patients using MRI-based radiomics and machine learning
. NMR in Biomedicine
.2022
;(35):e4792
|
|
| 31. |
2022
Le NQK,Ho QT,Nguyen VN,Chang JS.
BERT-Promoter: an improved sequence-based predictor of DNA promoter using BERT pre-trained model and SHAP feature selection
. Computational Biology and Chemistry
.2022
;(99):107732
|
|
| 32. |
2022
Lam LHT,Chu NT,Tran TO,Do DT,Le NQK.
A radiomics-based machine learning model for prediction of tumor mutational burden in lower-grade gliomas
. Cancers
.2022
;(14):3492
|
|
| 33. |
2022
Dang HH,Ta HDK,Nguyen TTT,Anuraga G,Wang CY,Lee KH, Le NQK.
Prospective role and immunotherapeutic targets of sideroflexin protein family in lung adenocarcinoma: evidence from bioinformatics validation
. Functional & Integrative Genomics
.2022
;(22):pages1057-1072
|
|
| 34. |
2022
Do DT,Yang MR,Lam LHT,Le NQK,Wu YW.
Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach
. Scientific Reports
.2022
;(12):13412
|
|
| 35. |
2022
Liu CM,Ta VD,Le NQK,Tadesse DA,Shi C.
Deep Neural Network Framework Based on Word Embedding for Protein Glutarylation Sites Prediction
. Life
.2022
;(12):1213
|
|
| 36. |
2022
Lesmana MHS,Le NQK,Chiu WC,Chung KH,Wang CY,Irham LM, Chung MH.
Genomic-Analysis-Oriented Drug Repurposing in the Search for Novel Antidepressants
. Biomedicines
.2022
;(10):1947
|
|
| 37. |
2022
Ho DKN,Lee YC,Chiu WC,Shen Y,Yao CY,Chu HK, Chu WT, Le NQK, Nguyen HT, Su HY, Chang JS.
COVID-19 and Virtual Nutrition: Integrating Digital Food Models for Interactive Portion-Size Education
. Nutrients
.2022
;(14):3313
|
|
| 38. |
2022
Kha QH,Tran TO,Nguyen TTD,Nguyen VN,Than K,Le NQK.
An interpretable deep learning model for classifying adaptor protein complexes from sequence information
. Methods
.2022
;(207):90-96
|
|
| 39. |
2022
Kha QH,Ho QT,Le NQK.
Identifying SNARE Proteins Using an Alignment-Free Method Based on Multiscan Convolutional Neural Network and PSSM Profiles
. Journal of Chemical Information and Modeling
.2022
;(62):4820-4826
|
|
| 40. |
2022
Zhao Z,Gui J,Yao A,Le NQK,Chua MCH.
Improved Prediction Model of Protein and Peptide Toxicity by Integrating Channel Attention into a Convolutional Neural Network and Gated Recurrent Units
. ACS Omega
.2022
;(7):40569-40577
|
|
| 41. |
2022
Hsu JC,Nguyen PA,Phuc PT,Lo TC,Hsu MH,Hsieh MS, Le NQK, Cheng CT, Chang TH, Chen CY.
Development and Validation of Novel Deep-Learning Models Using Multiple Data Types for Lung Cancer Survival
. Cancers
.2022
;(14):5562
|
|
| 42. |
2021
Le NQK,Ho QT,Nguyen TTD,Ou YY.
A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information
. Briefings in Bioinformatics
.2021
;(22):bbab005
|
|
| 43. |
2021
Ho QT,Nguyen TTD,Le NQK,Ou YY.
FAD-BERT: Improved Prediction of FAD Binding Sites Using Pre-training of Deep Bidirectional Transformers
. Computers in Biology and Medicine
.2021
;(131):104258
|
|
| 44. |
2021
Le NQK,Truong NKH,Do DT,Luu HTL,Luong HD,Huynh TT.
Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI
. Computers in Biology and Medicine
.2021
;(132):104320
|
|
| 45. |
2021
Le NQK,Do DT,Nguyen TTD,Le QA.
A sequence-based prediction of Kruppel-like factors proteins using XGBoost and optimized features
. Gene
.2021
;(787):145643
|
|
| 46. |
2021
Chiu FY,Le NQK,Chen CY.
A Multiparametric MRI-Based Radiomics Analysis to Efficiently Classify Tumor Subregions of Glioblastoma: A Pilot Study in Machine Learning
. Journal of Clinical Medicine
.2021
;(10):2030
|
|
| 47. |
2021
Nguyen TTD,Tran TA,Le NQK,Pham DM,Ou YY.
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
.2021
|
|
| 48. |
2021
Huynh TT,Lin CM,Le NQK,Vu MT,Nguyen NP,Chao F.
Intelligent wavelet fuzzy brain emotional controller using dual function-link network for uncertain nonlinear control systems
. Applied Intelligence
.2021
|
|
| 49. |
2021
Le VH,Kha QH,Truong NKH,Le NQK.
Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer
. Cancers
.2021
;(13):3616
|
|
| 50. |
2021
Le NQK,Kha QH,Nguyen VH,Chen YC,Cheng SJ,Chen CY.
Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer
. International Journal of Molecular Sciences
.2021
;(22):9254
|
|
| 51. |
2021
Dang HH,Ta HDK,Nguyen TTT,Anuraga G,Wang CY,Lee KH, Le NQK.
Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis
. Biomedicines
.2021
;(9):1144
|
|
| 52. |
2021
Ho QT,Le NQK,Ou YY.
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
.2021
|
|
| 53. |
2021
Hsu JBK,Lee TY,Cheng SJ,Lee GA,Chen YC,Le NQK, Huang SW, Kuo DP, Li YT, Chang TH, Chen CY.
Identification of Differentially Expressed Genes in Different Glioblastoma Regions and Their Association with Cancer Stem Cell Development and Temozolomide Response
. Journal of Personalized Medicine
.2021
;(11):1047
|
|
| 54. |
2021
Kha QH,Le VH,Hung TNK,Le NQK.
Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas
. Cancers
.2021
;(13):5398
|
|
| 55. |
2021
Le NQK.
Potential of deep representative learning features to interpret the sequence information in proteomics
. PROTEOMICS
.2021
;(22):1-2
|
|
| 56. |
2021
Dang LH,Dung NT,Quang LX,Hung LQ,Le NH,Le NTN, Diem NT, Nga NTT, Hung SH, Le NQK.
Machine learning-based prediction of drug-drug interactions for histamine antagonist using hybrid chemical features
. Cells
.2021
;(10):3092
|
|
| 57. |
2021
Tng SS,Le NQK,Yeh HY,Chua MCH.
Improved Prediction Model of Protein Lysine Crotonylation Sites Using Bidirectional Recurrent Neural Networks
. Journal of Proteome Research
.2021
;(21):256-273
|
|
| 58. |
2021
Nguyen TTD,Trinh VN,Le NQK,Ou YY.
Using k-mer embeddings learned from a Skip-gram based neural network for building a cross-species DNA N6-methyladenine site prediction model
. Plant Molecular Biology
.2021
;(107):533-542
|
|
| 59. |
2021
Nguyen TTD,Le NQK,Tran TA,Pham DM,Ou YY.
Incorporating a transfer learning technique with amino acid embeddings to efficiently predict N-linked glycosylation sites in ion channels
. Computers in Biology and Medicine
.2021
;(130):104212
|
|
| 60. |
2020
Do DT,Le NQK.
Using extreme gradient boosting to identify origin of replication in Saccharomyces cerevisiae via hybrid features
. Genomics
.2020
;(112):2445-2451
|
|
| 61. |
2020
Do DT,Le TQT,Le NQK.
Using deep neural networks and biological sub-words to detect protein S-sulfenylation sites
. Briefings in Bioinformatics
.2020
;(22)
|
|
| 62. |
2020
Nguyen TTD,Le NQK,Phan DV,Ho QT,Ou YY.
Using language representation learning approach to efficiently identify protein complex categories in electron transport chain
. Molecular Informatics
.2020
;(39):2000033
|
|
| 63. |
2020
Nguyen TTD,Ho QT,Le NQK,Phan DV,Ou YY.
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
.2020
|
|
| 64. |
2020
Le NQK,Do DT,Chiu FY,Yapp EKY,Yeh HY,Chen CY.
XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma
. Journal of Personalized Medicine
.2020
;(10)
|
|
| 65. |
2020
Sua JN,Lim SY,Yulius MH,Su X,Yapp EKY,Le NQK, Yeh HY, Chua MCH.
Incorporating convolutional neural networks and sequence graph transform for identifying multilabel protein Lysine PTM sites
. Chemometrics and Intelligent Laboratory Systems
.2020
;(206):104171
|
|
| 66. |
2020
Luu HTL,Le NH,Le VT,Ho TB,Truong NKH,Nguyen NTK, Luong HD, Le NQK.
Machine Learning Model for Identifying Antioxidant Proteins Using Features Calculated from Primary Sequences
. Biology
.2020
;(9(10))
|
|
| 67. |
2020
Hsu JBK,Lee GA,Chang TH,Huang SW,Le NQK,Chen YC, Kuo DP, Li YT, Chen CY.
Radiomic Immunophenotyping of GSEA-Assessed Immunophenotypes of Glioblastoma and Its Implications for Prognosis: A Feasibility Study
. Cancers
.2020
;(12(10))
|
|
| 68. |
2020
Nguyen TTD,Le NQK,Ho QT,Phan DV,Ou YY.
TNFPred: identifying tumor necrosis factors using hybrid features based on word embeddings
. BMC Medical Genomics
.2020
;(13)
|
|
| 69. |
2020
Le NQK,Do DT,Truong NKH,Luu HTL,Huynh TT,Nguyen NTK.
A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification
. International Journal of Molecular Sciences
.2020
;(21(23))
|
|
| 70. |
2020
Huynh TT,Lin CM,Le TL,Le NQK,Vu VP,Chao F.
Self-Organizing Double Function-Link Fuzzy Brain Emotional Control System Design for Uncertain Nonlinear Systems
. IEEE Transactions on Systems, Man, and Cybernetics: Systems
.2020
|
|
| 71. |
2019
Le NQK,Nguyen BP.
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
.2019
|
|
| 72. |
2019
Le NQK.
Fertility-GRU: Identifying Fertility-Related Proteins by Incorporating Deep-Gated Recurrent Units and Original Position-Specific Scoring Matrix Profiles
. Journal of Proteome Research
.2019
;(18):3503-3511
|
|
| 73. |
2019
Le NQK,Yapp EKY,Yeh HY.
ET-GRU: using multi-layer gated recurrent units to identify electron transport proteins
. BMC Bioinformatics
.2019
;(20):377-377
|
|
| 74. |
2019
Le NQK,Huynh TT,Yapp EKY,Yeh HY.
Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles
. Computer Methods and Programs in Biomedicine
.2019
;(177):81-88
|
|
| 75. |
2019
Do DT,Le NQK.
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
.2019
;(194):103855-103855
|
|
| 76. |
2019
Le NQK,Ho QT,Yapp EKY,Ou YY,Yeh HY.
DeepETC: A deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes
. Neurocomputing
.2019
;(375):71-79
|
|
| 77. |
2019
Le NQK,Yapp EKY,Nagasundaram N,Chua MCH,Yeh HY.
Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture
. Computational and Structural Biotechnology Journal
.2019
;(17):1245-1254
|
|
| 78. |
2019
Le NQK,Yapp EKY,Nagasundaram N,Yeh HY.
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
.2019
;(7)
|
|
| 79. |
2019
Le NQK,Huynh TT.
Identifying SNAREs by Incorporating Deep Learning Architecture and Amino Acid Embedding Representation
. Frontiers in Physiology
.2019
|
|
| 80. |
2019
Nguyen QH,Nguyen-Vo TH,Le NQK,Do TTT,Rahardja S,Nguyen BP.
iEnhancer-ECNN: Identifying enhancers and their strength using Ensemble of Convolutional Neural Networks
. BMC Genomics
.2019
;(20):951
|
|
| 81. |
2019
Le NQK,Nguyen QH,Chen X,Rahardja S,Nguyen BP.
Classification of Adaptor Proteins using Recurrent Neural Networks and PSSM Profiles
. BMC Genomics
.2019
;(20):966
|
|
| 82. |
2019
Huynh TT,Lin CM,Le TL,Cho HY,Pham TTT,Le NQK, Chao Fei.
A New Self-Organizing Fuzzy Cerebellar Model Articulation Controller for Uncertain Nonlinear Systems Using Overlapped Gaussian Membership Functions
. IEEE Transactions on Industrial Electronics
.2019
|
|
| 83. |
2019
Nagarajan N,Yapp EKY,Le NQK,Kamaraj B,Al-Subaie AM,Yeh HY.
Application of computational biology and artificial intelligence technologies in cancer precision drug discovery
. BioMed Research International
.2019
;(2019)
|
|
| 84. |
2019
Tan KK,Le NQK,Yeh HY,Chua MCH.
Ensemble of Deep Recurrent Neural Networks for Identifying Enhancers via Dinucleotide Physicochemical Properties
. Cells
.2019
;(8):767
|
|
| 85. |
2019
Nguyen TTD,Le NQK,Kusuma RMI,Ou YY.
Prediction of ATP-binding sites in membrane proteins using a two-dimensional convolutional neural network
. Journal of Molecular Graphics and Modelling
.2019
;(92):86-93
|
|
| 86. |
2019
Le NQK.
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
.2019
;(294):1173-1182
|
|
| 87. |
2019
Nguyen TTD,Le NQK,Ho QT,Phan DV,Ou YY.
Using word embedding technique to efficiently represent protein sequences for identifying substrate specificities of transporters
. Analytical Biochemistry
.2019
;(577):73-81
|
|
| 88. |
2019
Nagasundaram N,Yapp EKY,Le NQK,Yeh HY.
In silico screening of sugar alcohol compounds to inhibit viral matrix protein VP40 of Ebola virus
. Molecular Biology Reports
.2019
;(46):3315-3324
|
|
| 89. |
2019
Le NQK,Yapp EKY,Ou YY,Yeh HY.
iMotor-CNN: Identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou's 5-step rule
. Analytical Biochemistry
.2019
;(575):17-26
|
|
| 90. |
2019
Le NQK,Yapp EKY,Ho QT,Nagasundaram N,Ou YY,Yeh HY.
iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding
. Analytical Biochemistry
.2019
;(571):53-61
|
|
| 91. |
2019
Le NQK,Ho QT,Ou YY.
Using two-dimensional convolutional neural networks for identifying GTP binding sites in Rab proteins
. Journal of Bioinformatics and Computational Biology
.2019
;(17):195005
|
|
| 92. |
2019
Le NQK,Nguyen VN.
SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data
. PeerJ Computer Science
.2019
;(5):e177
|
|
| 93. |
2018
Le NQK,Sandag GA,Ou YY.
Incorporating post translational modification information for enhancing the predictive performance of membrane transport proteins
. Computational Biology and Chemistry
.2018
;(77):251-260
|
|
| 94. |
2018
Le NQK,Ho QT,Ou YY.
Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks
. Analytical Biochemistry
.2018
;(555):33-41
|
|
| 95. |
2018
Taju SW,Nguyen TTD,Le NQK,Kusuma RMI,Ou YY.
DeepEfflux: a 2D convolutional neural network model for identifying families of efflux proteins in transporters
. Bioinformatics
.2018
;(34):3111-3117
|
|
| 96. |
2017
Le NQK,Ho QT,Ou YY.
Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins
. Journal of Computational Chemistry
.2017
;(38):2000-2006
|
|
| 97. |
2017
Le NQK,Nguyen TTD,Ou YY.
Identifying the molecular functions of electron transport proteins using radial basis function networks and biochemical properties
. Journal of Molecular Graphics and Modelling
.2017
;(73):166-178
|
|
| 98. |
2016
Le NQK,Ou YY.
Incorporating efficient radial basis function networks and significant amino acid pairs for predicting GTP binding sites in transport proteins
. BMC Bioinformatics
.2016
;(17)
|
|
| 99. |
2016
Le NQK,Ou YY.
Prediction of FAD binding sites in electron transport proteins according to efficient radial basis function networks and significant amino acid pairs
. BMC Bioinformatics
.2016
;(17):298-298
|
|