World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
7246
World Ranking
6597
National Ranking
2915

Jijun Tang 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 Jijun Tang 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: 232 publications — 57th percentile

57% of scientists in this discipline score the same or lower.

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

Jijun Tang 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 Jijun Tang 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: 47 D-Index — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Overview

Jijun Tang is affiliated with the University of South Carolina in the United States. Their research spans the intersection of biochemistry, genetics, molecular biology, and computer science, with a focus on various aspects of bioinformatics and computational biology. The primary fields of study include Biochemistry, Genetics and Molecular Biology, as well as Computer Science.

The scientist's research subfields cover Molecular Biology, Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Theory and Mathematics, and Cancer Research. The main research topics include Machine Learning in Bioinformatics, RNA and protein synthesis mechanisms, Computational Drug Discovery Methods, Genomics and Phylogenetic Studies, Bioinformatics and Genomic Networks, Gene expression and cancer classification, and Advanced Neural Network Applications.

Jijun Tang has published extensively in a range of venues. Frequent publication venues include Briefings in Bioinformatics, IEEE/ACM Transactions on Computational Biology and Bioinformatics, the 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Current Bioinformatics, and Applied Soft Computing.

Among selected recent papers are:

  • Identification of Drug-Target Interactions via Dual Laplacian Regularized Least Squares with Multiple Kernel Fusion, 2020, Knowledge-Based Systems
  • DeepAVP: A Dual-Channel Deep Neural Network for Identifying Variable-Length Antiviral Peptides, 2020, IEEE Journal of Biomedical and Health Informatics
  • A comprehensive overview and critical evaluation of gene regulatory network inference technologies, 2021, Briefings in Bioinformatics
  • Exploring associations of non-coding RNAs in human diseases via three-matrix factorization with hypergraph-regular terms on center kernel alignment, 2020, Briefings in Bioinformatics
  • A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data, 2021, Briefings in Bioinformatics

Jijun Tang collaborates frequently with other researchers. Notable co-authors include Fei Guo, Yijie Ding, Limin Jiang, Yan Guo, and Shiqiang Ma.

Best Publications

  • Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information

    Leyi Wei;Jijun Tang;Jijun Tang;Quan Zou

  • Prediction of human protein subcellular localization using deep learning

    Leyi Wei;Leyi Wei;Yijie Ding;Ran Su;Ran Su;Jijun Tang

  • Identification of drug-side effect association via multiple information integration with centered kernel alignment

    Yijie Ding;Yijie Ding;Jijun Tang;Jijun Tang;Fei Guo

  • Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus

    Yan Zhang;Lin An;Jie Xu;Bo Zhang

  • Identification of drug-target interactions via multiple information integration

    Yijie Ding;Jijun Tang;Jijun Tang;Fei Guo

  • Steps toward accurate reconstructions of phylogenies from gene-order data

    Bernard M. E. Moret;Jijun Tang;Li-San Wang;Tandy Warnow

  • Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy

    Quan Zou;Shixiang Wan;Shixiang Wan;Ying Ju;Jijun Tang;Jijun Tang

  • A computational approach for examining the roots and spreading patterns of fake news: Evolution tree analysis

    S. Mo Jang;Tieming Geng;Jo-Yun Queenie Li;Ruofan Xia

  • Predicting protein-protein interactions via multivariate mutual information of protein sequences

    Yijie Ding;Jijun Tang;Jijun Tang;Fei Guo

  • Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC

    Yinan Shen;Jijun Tang;Jijun Tang;Fei Guo

  • Identification of Drug–Target Interactions via Dual Laplacian Regularized Least Squares with Multiple Kernel Fusion

    Yijie Ding;Jijun Tang;Jijun Tang;Fei Guo

  • SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides

    Leyi Wei;Leyi Wei;Jijun Tang;Quan Zou

  • MLGO: phylogeny reconstruction and ancestral inference from gene-order data.

    Fei Hu;Fei Hu;Yu Lin;Jijun Tang;Jijun Tang

  • PhosPred-RF: A Novel Sequence-Based Predictor for Phosphorylation Sites Using Sequential Information Only

    Leyi Wei;Pengwei Xing;Jijun Tang;Quan Zou

  • A comprehensive overview and critical evaluation of gene regulatory network inference technologies

    Mengyuan Zhao;Wenying He;Jijun Tang;Quan Zou

  • Inversion Medians Outperform Breakpoint Medians in Phylogeny Reconstruction from Gene-Order Data

    Bernard M. E. Moret;Adam C. Siepel;Jijun Tang;Tao Liu

  • Scaling up accurate phylogenetic reconstruction from gene-order data

    Jijun Tang;Bernard M. E. Moret

  • DeepAVP: A Dual-Channel Deep Neural Network for Identifying Variable-Length Antiviral Peptides

    Jiawei Li;Yuqian Pu;Jijun Tang;Quan Zou

  • Identification of membrane protein types via multivariate information fusion with Hilbert–Schmidt Independence Criterion

    Hao Wang;Yijie Ding;Jijun Tang;Jijun Tang;Fei Guo

  • MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

    Yi Zou;Hongjie Wu;Xiaoyi Guo;Li Peng

  • Identification of Protein–Protein Interactions via a Novel Matrix-Based Sequence Representation Model with Amino Acid Contact Information

    Yijie Ding;Jijun Tang;Jijun Tang;Fei Guo

  • Identification of drug–target interactions via fuzzy bipartite local model

    Yijie Ding;Jijun Tang;Jijun Tang;Fei Guo

  • Reconstructing phylogenies from gene-content and gene-order data

    Bernard M. E. Moret;Jijun Tang;Tandy J. Warnow

Frequent Co-Authors

Bernard M. E. Moret
Bernard M. E. Moret École Polytechnique Fédérale de Lausanne
Quan Zou
Quan Zou University of Electronic Science and Technology of China
Leyi Wei
Leyi Wei Shandong University
Roger A. Dougal
Roger A. Dougal University of South Carolina
Claude W. dePamphilis
Claude W. dePamphilis Pennsylvania State University
David A. Bader
David A. Bader New Jersey Institute of Technology
David C. Samuels
David C. Samuels Vanderbilt University
Hao Wang
Hao Wang Tianjin University
Li-San Wang
Li-San Wang University of Pennsylvania
Austin L. Hughes
Austin L. Hughes University of South Carolina

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