World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
9547
World Ranking
4392
National Ranking
19

Xin Gao 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 Xin Gao 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: 296 publications — 73rd percentile

73% of scientists in this discipline score the same or lower.

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

Xin Gao 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 Xin Gao 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: 55 D-Index — 71st percentile

71% of scientists in this discipline score the same or lower.

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

Overview

Xin Gao is affiliated with King Abdullah University of Science and Technology in Saudi Arabia. Their research primarily focuses on the field of Biochemistry, Genetics, and Molecular Biology, with a significant emphasis on Molecular Biology, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Cancer Research, and Computer Vision and Pattern Recognition.

The scientist's work spans multiple main topics including RNA and protein synthesis mechanisms, Genomics and Phylogenetic Studies, Radiomics and Machine Learning in Medical Imaging, Computational Drug Discovery Methods, RNA modifications and cancer, Topic Modeling, and Bioinformatics and Genomic Networks.

Xin Gao has contributed to a number of recent papers, including:

  • Opportunities and challenges for ChatGPT and large language models in biomedicine and health (2023), Briefings in Bioinformatics
  • Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024 (2023), Nucleic Acids Research
  • Towards artificial general intelligence via a multimodal foundation model (2022), Nature Communications
  • A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis (2020), IEEE Transactions on Medical Imaging
  • A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics (2023), Nature Communications

Frequent co-authors collaborating with Xin Gao include:

  • Juexiao Zhou
  • Takashi Gojobori
  • Haoyang Li
  • Bin Zhang
  • Xiaopeng Xu

Xin Gao has published extensively in certain venues, with the most publications in:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Nature Communications
  • SSRN Electronic Journal
  • Briefings in Bioinformatics

In addition to journal articles, Xin Gao has contributed to book publications, including the title Analysing Religious Discourse (2021) published by Cambridge University Press.

Best Publications

  • Deep learning in bioinformatics: Introduction, application, and perspective in the big data era.

    Yu Li;Chao Huang;Lizhong Ding;Zhongxiao Li

  • DEEPre: sequence-based enzyme EC number prediction by deep learning

    Yu Li;Sheng Wang;Ramzan Umarov;Bingqing Xie

  • Bioinformatics clouds for big data manipulation.

    Lin Dai;Xin Gao;Yan Guo;Jingfa Xiao

  • A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis

    Longxi Zhou;Zhongxiao Li;Juexiao Zhou;Haoyang Li

  • Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis

    Abdul-Hamid M. Emwas;Raja Roy;Ryan T. McKay;Danielle Ryan

  • Machine learning and deep learning methods that use omics data for metastasis prediction

    Somayah Albaradei;Somayah Albaradei;Maha A. Thafar;Maha A. Thafar;Asim Alsaedi;Asim Alsaedi;Christophe Marc Van Neste

  • Recommended strategies for spectral processing and post-processing of 1D 1H-NMR data of biofluids with a particular focus on urine.

    Abdul-Hamid M. Emwas;Edoardo Saccenti;Xin Gao;Ryan T. McKay

  • Non-negative matrix factorization by maximizing correntropy for cancer clustering

    Jim Jing-Yan Wang;Xiaolei Wang;Xin Gao

  • Semantic similarity and machine learning with ontologies

    Maxat Kulmanov;Fatima Zohra Smaili;Xin Gao;Robert Hoehndorf

  • OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction.

    Fatima Zohra Smaili;Xin Gao;Robert Hoehndorf

  • Multiple graph regularized nonnegative matrix factorization

    Jim Jing-Yan Wang;Halima Bensmail;Xin Gao

  • Learning from Weak and Noisy Labels for Semantic Segmentation

    Zhiwu Lu;Zhenyong Fu;Tao Xiang;Peng Han

  • DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

    Maha A. Thafar;Maha A. Thafar;Rawan S. Olayan;Haitham Ashoor;Somayah Albaradei;Somayah Albaradei

  • Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations.

    Fatima Zohra Smaili;Xin Gao;Robert Hoehndorf

  • GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization

    Peng Han;Peng Yang;Peilin Zhao;Shuo Shang

  • Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence

    Yu-An Huang;Zhu-Hong You;Xin Gao;Leon Wong

  • mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning.

    Zhenzhen Zou;Shuye Tian;Xin Gao;Yu Li

  • Computer-aided drug repurposing for cancer therapy: Approaches and opportunities to challenge anticancer targets

    Carla Mottini;Francesco Napolitano;Zhongxiao Li;Xin Gao

  • A deep learning framework to predict binding preference of RNA constituents on protein surface

    Jordy Homing Lam;Jordy Homing Lam;Yu Li;Lizhe Zhu;Lizhe Zhu;Ramzan Umarov

  • DeepSimulator: a deep simulator for Nanopore sequencing.

    Yu Li;Renmin Han;Chongwei Bi;Mo Li

  • Promoter analysis and prediction in the human genome using sequence-based deep learning models.

    Ramzan Umarov;Hiroyuki Kuwahara;Yu Li;Xin Gao

  • Notice of Violation of IEEE Publication Principles Bag-of-Features Based Medical Image Retrieval via Multiple Assignment and Visual Words Weighting

    Jingyan Wang;Yongping Li;Ying Zhang;Chao Wang

  • Multimodal Machine Learning for Automated ICD Coding

    Keyang Xu;Mike Lam;Jingzhi Pang;Xin Gao

Frequent Co-Authors

Takashi Gojobori
Takashi Gojobori King Abdullah University of Science and Technology
Vladimir B. Bajic
Vladimir B. Bajic King Abdullah University of Science and Technology
Xuhui Huang
Xuhui Huang University of Wisconsin–Madison
Robert Hoehndorf
Robert Hoehndorf King Abdullah University of Science and Technology
Stefan T. Arold
Stefan T. Arold King Abdullah University of Science and Technology
Ivan Mijakovic
Ivan Mijakovic Chalmers University of Technology
Abdul-Hamid Emwas
Abdul-Hamid Emwas King Abdullah University of Science and Technology
Jinbo Xu
Jinbo Xu Toyota Technological Institute at Chicago
Ying Xu
Ying Xu University of Georgia
Peilin Zhao
Peilin Zhao Tencent (China)

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