World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
7562
World Ranking
11062
National Ranking
4597

Xue-wen Chen 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 Xue-wen Chen 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: 121 publications — 15th percentile

15% of scientists in this discipline score the same or lower.

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

Xue-wen Chen 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 Xue-wen Chen 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: 36 D-Index — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Overview

Xue-wen Chen is affiliated with Wayne State University in the United States. Their research contributions span multiple specialized fields within engineering, with a primary focus on Electrical and Electronic Engineering, Biomedical Engineering, Materials Chemistry, Soil Science, and Atomic and Molecular Physics, and Optics.

The body of work produced by Chen covers various main topics, including:

  • Soil Carbon and Nitrogen Dynamics
  • Plasmonic and Surface Plasmon Research
  • Near-Field Optical Microscopy
  • Photonic and Optical Devices
  • Metallurgy and Material Forming
  • Photonic Crystals and Applications
  • Gold and Silver Nanoparticles Synthesis and Applications

Chen has published their research in a diverse range of scientific venues. The most frequent publication sources include:

  • arXiv (Cornell University)
  • Materials
  • SSRN Electronic Journal
  • Soil and Tillage Research
  • European Journal of Soil Biology

Some recent noteworthy papers exemplify the scope of their work:

  • Bright single-nanocrystal upconversion at sub 0.5 W cm−2 irradiance via coupling to single nanocavity mode, 2022, Nature Photonics
  • A Novel Quantitative Index of Meibomian Gland Dysfunction, the Meibomian Gland Tortuosity, 2020, Translational Vision Science & Technology
  • Effect of long-term tillage and cropping system on portion of fungal and bacterial necromass carbon in soil organic carbon, 2021, Soil and Tillage Research
  • Greater fungal and bacterial biomass in soil large macropores under no-tillage than mouldboard ploughing, 2020, European Journal of Soil Biology
  • The impact of cropping system, tillage and season on shaping soil fungal community in a long-term field trial, 2020, European Journal of Soil Biology

Chen frequently collaborates with a core group of co-authors. These include:

  • Aizhen Liang
  • Shixiu Zhang
  • Neil B. McLaughlin
  • Pu Zhang
  • Yan Gao

This collaboration indicates a multidisciplinary approach and active participation in joint research efforts.

Best Publications

  • Big Data Deep Learning: Challenges and Perspectives

    Xue-Wen Chen;Xiaotong Lin

  • Machine learning and its applications to biology.

    Adi L Tarca;Vincent J Carey;Xue-wen Chen;Roberto Romero

  • Prediction of protein--protein interactions using random decision forest framework

    Xue-Wen Chen;Mei Liu

  • Combating the Small Sample Class Imbalance Problem Using Feature Selection

    M Wasikowski;Xue-wen Chen

  • Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs

    Mei Liu;Yonghui Wu;Yukun Chen;Jingchun Sun

  • FAST: a roc-based feature selection metric for small samples and imbalanced data classification problems

    Xue-wen Chen;Michael Wasikowski

  • Improving Bayesian Network Structure Learning with Mutual Information-Based Node Ordering in the K2 Algorithm

    Xue-Wen Chen;G. Anantha;Xiaotong Lin

  • On Position-Specific Scoring Matrix for Protein Function Prediction

    Jong cheol Jeong;Xiaotong Lin;Xue-wen Chen

  • Learning Deep Networks from Noisy Labels with Dropout Regularization

    Ishan Jindal;Matthew Nokleby;Xuewen Chen

  • Facial expression recognition: a clustering-based approach

    Xue-wen Chen;Thomas Huang

  • Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data

    J. S. Yu;S. Ongarello;R. Fiedler;X. W. Chen

  • Sequence-based prediction of protein interaction sites with an integrative method

    Xue-wen Chen;Jong Cheol Jeong

  • Sparse representation and learning in visual recognition: Theory and applications

    Hong Cheng;Zicheng Liu;Lu Yang;Xuewen Chen

  • An improved branch and bound algorithm for feature selection

    Xue-wen Chen

  • An effective structure learning method for constructing gene networks

    Xue-Wen Chen;Gopalakrishna Anantha;Xinkun Wang

  • SMO-based pruning methods for sparse least squares support vector machines

    Xiangyan Zeng;Xue-wen Chen

  • Mining adverse drug reactions from online healthcare forums using hidden Markov model.

    Hariprasad Sampathkumar;Xue wen Chen;Bo Luo

  • A Markov blanket-based method for detecting causal SNPs in GWAS

    Bing Han;Meeyoung Park;Xue wen Chen

  • Enhanced recursive feature elimination

    Xue-wen Chen;Jong Cheol Jeong

  • Bayesian neural network approaches to ovarian cancer identification from high-resolution mass spectrometry data

    Jiangsheng Yu;Xue-Wen Chen

  • Kernel-based distance metric learning for microarray data classification

    Huilin Xiong;Xue Wen Chen

Frequent Co-Authors

Hong Cheng
Hong Cheng University of Electronic Science and Technology of China
David Casasent
David Casasent Carnegie Mellon University
Ya Zhang
Ya Zhang Shanghai Jiao Tong University
Silvia Conforto
Silvia Conforto Roma Tre University
Hua Xu
Hua Xu Yale University
Thomas S. Huang
Thomas S. Huang University of Illinois at Urbana-Champaign
Zicheng Liu
Zicheng Liu Microsoft (United States)
Ruoming Jin
Ruoming Jin Kent State University
Zhongming Zhao
Zhongming Zhao The University of Texas Health Science Center at Houston
Xun Wang
Xun Wang Tsinghua University

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