World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
5021
World Ranking
11727
National Ranking
1450

Maozu Guo 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 Maozu Guo 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: 247 publications — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Maozu Guo 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 Maozu Guo 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: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

Maozu Guo is affiliated with Beijing University of Civil Engineering and Architecture in China. Their research spans multiple fields with a primary focus on Biochemistry, Genetics and Molecular Biology, as well as Computer Science. This interdisciplinary approach is reflected in the broad range of topics they explore.

The scientist's main areas of study and expertise include:

  • Biochemistry, Genetics and Molecular Biology
  • Computer Science

Within these fields, Maozu Guo has contributed to various subfields, such as:

  • Molecular Biology
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Cancer Research
  • Civil and Structural Engineering

The research topics covered by this scientist feature:

  • Bioinformatics and Genomic Networks
  • Machine Learning in Bioinformatics
  • Gene expression and cancer classification
  • Topic Modeling
  • Genomics and Phylogenetic Studies
  • Biomedical Text Mining and Ontologies
  • Computational Drug Discovery Methods

Maozu Guo has published extensively in academic journals. The venues where their work appears most frequently include:

  • Briefings in Bioinformatics
  • IEEE Access
  • arXiv (Cornell University)
  • Frontiers in Genetics
  • Nucleic Acids Research

Selected recent papers of Maozu Guo provide insight into the focus and scope of their research:

  • A Literature Review of Gene Function Prediction by Modeling Gene Ontology (2020, Frontiers in Genetics)
  • Accurate and Efficient Indoor Pathfinding Based on Building Information Modeling Data (2020, IEEE Transactions on Industrial Informatics)
  • Predicting miRNA-disease associations using an ensemble learning framework with resampling method (2021, Briefings in Bioinformatics)
  • Predicting microbe-drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy (2023, Briefings in Bioinformatics)
  • sc2GWAS: a comprehensive platform linking single cell and GWAS traits of human (2024, Nucleic Acids Research)

The scientist frequently collaborates with a number of co-authors, including:

  • Guoxian Yu
  • Jun Wang
  • Xiangliang Zhang
  • Carlotta Domeniconi
  • Xiaoyan Liu

Best Publications

  • A comprehensive overview and evaluation of circular RNA detection tools.

    Xiangxiang Zeng;Wei Lin;Maozu Guo;Quan Zou

  • Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors

    Ping Xuan;Ke Han;Maozu Guo;Yahong Guo

  • Rank Entropy-Based Decision Trees for Monotonic Classification

    Qinghua Hu;Xunjian Che;Lei Zhang;D. Zhang

  • HAlign: Fast Multiple Similar DNA/RNA Sequence Alignment Based on the Centre Star Strategy

    Quan Zou;Qinghua Hu;Maozu Guo;Guohua Wang

  • Fuzzy preference based rough sets

    Qinghua Hu;Daren Yu;Maozu Guo

  • Feature Selection for Monotonic Classification

    Qinghua Hu;Weiwei Pan;Lei Zhang;D. Zhang

  • Simple sequence-based kernels do not predict protein-protein interactions

    Unknown

  • Information entropy for ordinal classification

    QingHua Hu;MaoZu Guo;DaRen Yu;JinFu Liu

  • An overview of SNP interactions in genome-wide association studies

    Pei Li;Maozu Guo;Chunyu Wang;Xiaoyan Liu

  • Measuring gene functional similarity based on group-wise comparison of GO terms

    Unknown

  • An Improved K-medoids Algorithm Based on Step Increasing and Optimizing Medoids

    Donghua Yu;Guojun Liu;Maozu Guo;Xiaoyan Liu

  • PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAs.

    Ping Xuan;Maozu Guo;Xiaoyan Liu;Yangchao Huang

  • A Literature Review of Gene Function Prediction by Modeling Gene Ontology.

    Yingwen Zhao;Jun Wang;Jian Chen;Xiangliang Zhang

  • MiRTDL: A Deep Learning Approach for miRNA Target Prediction

    Shuang Cheng;Maozu Guo;Chunyu Wang;Xiaoyan Liu

  • Density Peaks Clustering Based on Weighted Local Density Sequence and Nearest Neighbor Assignment

    Donghua Yu;Guojun Liu;Maozu Guo;Xiaoyan Liu

  • MaturePred: efficient identification of microRNAs within novel plant pre-miRNAs.

    Ping Xuan;Ping Xuan;Maozu Guo;Yangchao Huang;Wenbin Li

  • Accurate and Efficient Indoor Pathfinding Based on Building Information Modeling Data

    Unknown

  • A novel insight into Gene Ontology semantic similarity.

    Yungang Xu;Maozu Guo;Wenli Shi;Xiaoyan Liu

  • Ranking-Based Deep Cross-Modal Hashing

    Xuanwu Liu;Guoxian Yu;Carlotta Domeniconi;Jun Wang

  • Correction: Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors

    Ping Xuan;Ke Han;Maozu Guo;Yahong Guo

  • A new co-training-style random forest for computer aided diagnosis

    Unknown

  • Erratum: Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors (PLoS ONE (2013) 8 (8))

    Ping Xuan;Ke Han;Jin Li;Maozu Guo

  • A Cancer Survival Prediction Method Based on Graph Convolutional Network

    Chunyu Wang;Junling Guo;Ning Zhao;Yang Liu

  • Computational approaches in detecting non- coding RNA.

    Chunyu Wang;Leyi Wei;Maozu Guo;Quan Zou

  • Inferring the soybean (Glycine max) microRNA functional network based on target gene network.

    Yungang Xu;Maozu Guo;Xiaoyan Liu;Chunyu Wang

Frequent Co-Authors

Carlotta Domeniconi
Carlotta Domeniconi George Mason University
Xiangliang Zhang
Xiangliang Zhang University of Notre Dame
Quan Zou
Quan Zou University of Electronic Science and Technology of China
Yufei Huang
Yufei Huang University of Pittsburgh
Yazhou Ren
Yazhou Ren University of Electronic Science and Technology of China
Xiangxiang Zeng
Xiangxiang Zeng Hunan University
Lei Zhang
Lei Zhang Hong Kong Polytechnic University
Chenxi Wu
Chenxi Wu Chinese Academy of Sciences
Rui Zhang
Rui Zhang National University of Singapore
Leyi Wei
Leyi Wei Shandong University

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