World's Best Scientists 2026 revealed!
Yanchun Liang

Yanchun Liang

D-Index & Metrics

Computer Science

D-Index
49
Citations
11254
World Ranking
5830
National Ranking
772

Yanchun Liang 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 Yanchun Liang 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: 313 publications — 76th percentile

76% of scientists in this discipline score the same or lower.

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

Yanchun Liang 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 Yanchun Liang 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: 49 D-Index — 60th percentile

60% of scientists in this discipline score the same or lower.

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

Overview

Yanchun Liang is affiliated with Jilin University in China and has contributed extensively to the fields of Computer Science and Engineering. Their research includes a significant focus on Artificial Intelligence, Molecular Biology, and Control and Systems Engineering, with additional work in Computer Vision and Pattern Recognition as well as Mechanical Engineering.

Their academic output covers a wide range of topics, including:

  • Elevator Systems and Control
  • Metaheuristic Optimization Algorithms Research
  • Machine Learning in Bioinformatics
  • Gene expression and cancer classification
  • Single-cell and spatial transcriptomics
  • Power Line Inspection Robots
  • Topic Modeling

Yanchun Liang's recent publications demonstrate engagement in both theoretical and applied research across interdisciplinary domains. Notable papers include:

  • "The bioinformatics toolbox for circRNA discovery and analysis," 2020, published in Briefings in Bioinformatics
  • "Deep Feature-Based Text Clustering and its Explanation," 2020, IEEE Transactions on Knowledge and Data Engineering
  • "Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods," 2023, Entropy
  • "Deep Attention Diffusion Graph Neural Networks for Text Classification," 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • "Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study," 2020, International Journal of Molecular Sciences

The publication venues reflect a multidisciplinary interest with frequent contributions to:

  • Entropy
  • Journal of Physics Conference Series
  • arXiv (Cornell University)
  • International Journal of Molecular Sciences
  • Electronics

Yanchun Liang has collaborated regularly with several co-authors, including Renchu Guan, Chunguo Wu, Shuangchang Feng, Dong Xu, and Adriano Tavares, indicating an active network within their research community.

Best Publications

  • Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning

    Chen Yang;Chen Yang;Haishi Zhao;Lorenzo Bruzzone;Jon Atli Benediktsson

  • PROPER ORTHOGONAL DECOMPOSITION AND ITS APPLICATIONS—PART I: THEORY

    Y.C. Liang;H.P. Lee;S.P. Lim;W.Z. Lin

  • Particle swarm optimization-based algorithms for TSP and generalized TSP

    X. H. Shi;Y. C. Liang;H. P. Lee;C. Lu

  • An improved GA and a novel PSO-GA-based hybrid algorithm

    X. H. Shi;Y. C. Liang;H. P. Lee;C. Lu

  • The bioinformatics toolbox for circRNA discovery and analysis.

    Liang Chen;Changliang Wang;Huiyan Sun;Juexin Wang

  • An ant colony optimization method for generalized TSP problem

    Jinhui Yang;Xiaohu Shi;Maurizio Marchese;Yanchun Liang

  • Text classification based on deep belief network and softmax regression

    Mingyang Jiang;Yanchun Liang;Xiaoyue Feng;Xiaojing Fan

  • MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction.

    Duolin Wang;Shuai Zeng;Chunhui Xu;Wangren Qiu;Wangren Qiu

  • Using Internet Search Engines to Obtain Medical Information: A Comparative Study

    Liupu Wang;Juexin Wang;Michael Wang;Yong Li

  • Particle swarm optimization based on dimensional learning strategy

    Guiping Xu;Quanlong Cui;Xiaohu Shi;Hongwei Ge

  • A novel quantum swarm evolutionary algorithm and its applications

    Yan Wang;Xiao-Yue Feng;Yan-Xin Huang;Dong-Bing Pu

  • Text Clustering with Seeds Affinity Propagation

    Renchu Guan;Xiaohu Shi;M Marchese;Chen Yang

  • PROPER ORTHOGONAL DECOMPOSITION AND ITS APPLICATIONS – PART II: MODEL REDUCTION FOR MEMS DYNAMICAL ANALYSIS

    Y.C. Liang;W.Z. Lin;H.P. Lee;S.P. Lim

  • LncFinder: an integrated platform for long non-coding RNA identification utilizing sequence intrinsic composition, structural information and physicochemical property.

    Siyu Han;Yanchun Liang;Qin Ma;Yangyi Xu

  • Hybrid evolutionary algorithms based on PSO and GA

    X.H. Shi;Y.H. Lu;C.G. Zhou;H.P. Lee

  • Capsule network for protein post-translational modification site prediction.

    Duolin Wang;Duolin Wang;Yanchun Liang;Dong Xu;Dong Xu

  • Lunar impact craters identification and age estimation with Chang'E data by deep and transfer learning

    Chen Yang;Haishi Zhao;Lorenzo Bruzzone;Jon Atli Benediktsson

  • A resampling ensemble algorithm for classification of imbalance problems

    Yun Qian;Yun Qian;Yanchun Liang;Mu Li;Guoxiang Feng

  • Clonal Selection Based Memetic Algorithm for Job Shop Scheduling Problems

    Jin-hui Yang;Liang Sun;Heow Pueh Lee;Heow Pueh Lee;Yun Qian

  • Deep Feature-Based Text Clustering and Its Explanation

    Renchu Guan;Hao Zhang;Yanchun Liang;Fausto Giunchiglia

  • An improved genetic algorithm with variable population-size and a PSO-GA based hybrid evolutionary algorithm

    X.H. Shi;L.M. Wan;H.P. Lee;X.W. Yang

  • Solving traveling salesman problems using generalized chromosome genetic algorithm

    Jinhui Yang;Chunguo Wu;Chunguo Wu;Heow Pueh Lee;Heow Pueh Lee;Yanchun Liang

Frequent Co-Authors

Dong Xu
Dong Xu University of Missouri
Heow Pueh Lee
Heow Pueh Lee National University of Singapore
Trupti Joshi
Trupti Joshi University of Missouri
Ying Xu
Ying Xu University of Georgia
Fausto Giunchiglia
Fausto Giunchiglia University of Trento
Bing-Hong Wang
Bing-Hong Wang University of Science and Technology of China
Limin Wang
Limin Wang Chinese Academy of Sciences
Lorenzo Bruzzone
Lorenzo Bruzzone University of Trento
Ying Li
Ying Li Nanjing Agricultural University
Xiaowei Yang
Xiaowei Yang Shanghai Jiao Tong University

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