World's Best Scientists 2026 revealed!
Nguyen Quoc Khanh Le

Nguyen Quoc Khanh Le

Award Badge
Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
41
Citations
3705
World Ranking
632
National Ranking
1

Computer Science

D-Index
46
Citations
4665
World Ranking
6959
National Ranking
59

Nguyen Quoc Khanh Le publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Nguyen Quoc Khanh Le sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 84 publications — 4th percentile

4% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Nguyen Quoc Khanh Le D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Nguyen Quoc Khanh Le sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 46 D-Index — 53rd percentile

53% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Nguyen Quoc Khanh Le is affiliated with Taipei Medical University in Taiwan and specializes in research at the intersection of biochemistry, genetics, molecular biology, and medicine. Their primary fields of study include Biochemistry, Genetics, and Molecular Biology, with a significant focus on Medicine.

The scientist's subfields cover Molecular Biology, Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Computational Theory and Mathematics, and Artificial Intelligence. Their research topics emphasize machine learning applications in bioinformatics and medical imaging, RNA and protein synthesis mechanisms, genomics and phylogenetic studies, computational drug discovery methods, vaccines, immunoinformatics approaches, and glioma diagnosis and treatment.

Their frequent publication venues include:

  • Computers in Biology and Medicine
  • Cancers
  • Briefings in Bioinformatics
  • Journal of the American College of Cardiology
  • Methods

Nguyen Quoc Khanh Le has collaborated extensively with several co-authors, including:

  • Le Huu Nhat Minh
  • Trinh-Trung-Duong Nguyen
  • Ngan Nguyen
  • Yu-Yen Ou
  • Quang-Thai Ho

Recent published papers by Nguyen Quoc Khanh Le illustrate their focus on machine learning and medical imaging analysis. These include:

  • A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information, 2021, Briefings in Bioinformatics
  • Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer, 2021, International Journal of Molecular Sciences
  • Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI, 2021, Computers in Biology and Medicine
  • XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma, 2020, Journal of Personalized Medicine

In addition, Nguyen Quoc Khanh Le's research intersects with explainable AI approaches, as shown in their involvement in "On the road to explainable AI in drug-drug interactions prediction: A systematic review," published in 2022 in the Computational and Structural Biotechnology Journal.

Best Publications

  • A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information

    Nguyen Quoc Khanh Le;Quang-Thai Ho;Trinh-Trung-Duong Nguyen;Yu-Yen Ou

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

    Nguyen Quoc Khanh Le;Edward Kien Yee Yapp;Quang-Thai Ho;Nagarajan Nagasundaram

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

    Nguyen Quoc Khanh Le;Nguyen Quoc Khanh Le;Quang Hien Kha;Van Hiep Nguyen;Yung-Chieh Chen

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

    Nguyen Quoc Khanh Le;Edward Kien Yee Yapp;Nagarajan Nagasundaram;Hui-Yuan Yeh

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

    Nguyen Quoc Khanh Le;Nguyen Quoc Khanh Le;Truong Nguyen Khanh Hung;Duyen Thi Do;Luu Ho Thanh Lam

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

    Nguyen Quoc Khanh Le;Quang Thai Ho;Yu Yen Ou

  • iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks.

    Quang H. Nguyen;Thanh Hoang Nguyen-Vo;Nguyen Quoc Khanh Le;Trang T.T. Do

  • Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles

    Nguyen Quoc Khanh Le;Tuan Tu Huynh;Edward Kien Yee Yapp;Hui Yuan Yeh

  • Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery

    Nagasundaram Nagarajan;Edward K.Y. Yapp;Nguyen Quoc Khanh Le;Balu Kamaraj

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

    Luu Ho Thanh Lam;Luu Ho Thanh Lam;Ngoc Hoang Le;Le Van Tuan;Ho Tran Ban

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

    Nguyen Quoc Khanh Le

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

    Nguyen Quoc Khanh Le;Quang Thai Ho;Yu Yen Ou

  • Using deep neural networks and biological subwords to detect protein S-sulfenylation sites

    Duyen Thi Do;Thanh Quynh Trang Le;Nguyen Quoc Khanh Le

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

    Nguyen Quoc Khanh Le;Nguyen Quoc Khanh Le;Duyen Thi Do;Truong Nguyen Khanh Hung;Truong Nguyen Khanh Hung;Luu Ho Thanh Lam;Luu Ho Thanh Lam

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

    Nguyen Quoc Khanh Le;Yu Yen Ou

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

    Nguyen Quoc Khanh Le;Trinh Trung Duong Nguyen;Yu Yen Ou

  • Improved Prediction Model of Protein Lysine Crotonylation Sites Using Bidirectional Recurrent Neural Networks.

    Sian Soo Tng;Nguyen Quoc Khanh Le;Nguyen Quoc Khanh Le;Hui-Yuan Yeh;Matthew Chin Heng Chua

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

    Nguyen Quoc Khanh Le;Edward Kien Yee Yapp;Hui Yuan Yeh

  • Potential of deep representative learning features to interpret the sequence information in proteomics.

    Nguyen Quoc Khanh Le

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

    Nguyen Quoc Khanh Le;Nguyen Quoc Khanh Le;Edward Kien Yee Yapp;Nagarajan Nagasundaram;Matthew Chin Heng Chua

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

    Nguyen Quoc Khanh Le;Van Nui Nguyen

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

    Nguyen Quoc Khanh Le;Edward Kien Yee Yapp;Yu Yen Ou;Hui Yuan Yeh

Frequent Co-Authors

Susanto Rahardja
Susanto Rahardja Northwestern Polytechnical University
Chih-Min Lin
Chih-Min Lin Yuan Ze University
Fei Chao
Fei Chao Xiamen University

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