Yi Xiong

Active 1990–2026

333
Papers
18,253
Citations
60
h-index
229
i10-index

Citations

Citations per year for Yi Xiong1966: 1 citations1978: 1 citations1980: 2 citations1982: 1 citations1997: 1 citations2000: 14 citations2001: 23 citations2002: 37 citations2003: 36 citations2004: 24 citations2005: 38 citations2006: 28 citations2007: 56 citations2008: 53 citations2009: 49 citations2010: 61 citations2011: 49 citations2012: 90 citations2013: 97 citations2014: 86 citations2015: 91 citations2016: 79 citations2017: 97 citations2018: 137 citations2019: 278 citations2020: 458 citations2021: 486 citations2022: 574 citations2023: 644 citations2024: 1,065 citations2025: 786 citations2026: 151 citations1967–1977: no citations, so these years are not shown1979: no citations, so this year is not shown1981: no citations, so this year is not shown1983–1996: no citations, so these years are not shown1998–1999: no citations, so these years are not shown

Citation sources

Countries

World map of the countries and regions citing this authorChina: 2,571 citing papers, 37.7% of this breakdownUnited States: 1,141 citing papers, 16.7% of this breakdownUnited Kingdom: 278 citing papers, 4.1% of this breakdownGermany: 185 citing papers, 2.7% of this breakdownIndia: 183 citing papers, 2.7% of this breakdownSouth Korea: 167 citing papers, 2.5% of this breakdownFrance: 150 citing papers, 2.2% of this breakdownJapan: 140 citing papers, 2.1% of this breakdownAustralia: 139 citing papers, 2% of this breakdownCanada: 136 citing papers, 2% of this breakdownItaly: 109 citing papers, 1.6% of this breakdownSpain: 105 citing papers, 1.5% of this breakdown
0%37.7%Other 22.2%

Fields

  • Biochemistry, Genetics and Molecular Biology32.4%
  • Medicine18.5%
  • Engineering15.3%
  • Computer Science15.2%
  • Immunology and Microbiology6.4%
  • Materials Science3.2%
  • Other9%

Topics

  • Computational Drug Discovery Methods3.5%
  • Machine Learning in Bioinformatics3.3%
  • Bioinformatics and Genomic Networks2%
  • Fungal and yeast genetics research1.7%
  • Ferroptosis and cancer prognosis1.7%
  • Protein Structure and Dynamics1.7%
  • Other86.1%

Coauthors

All papers

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  1. IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures

    Authors: , , , , , , , , , , , , , , - Frontiers in Immunology 2021 cited by 1,296

  2. MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism

    Authors: , , , , , , , , , , - Briefings in Bioinformatics, Briefings Bioinform. 2021 cited by 183

  3. A transformer-based model to predict peptide-HLA class I binding and optimize mutated peptides for vaccine design

    Authors: , , , , , , , , , , , - Nature Machine Intelligence, Nat. Mach. Intell. 2022 cited by 189

  4. MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning

    Authors: , , , , , - Journal of Cheminformatics, J. Cheminformatics 2022 cited by 56

  5. NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information

    Authors: , , , , , - Nucleic Acids Research, Nucleic Acids Res. 2021 cited by 129

  6. PLMSearch: Protein language model powers accurate and fast sequence search for remote homology

    Authors: , , , , , , , - Nature Communications 2024 cited by 86

  7. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features

    Authors: , , , , , , , , - Briefings in Bioinformatics, Briefings Bioinform. 2019 cited by 212

  8. Privacy-Preserved Average Consensus Algorithms with Edge-based Additive Perturbations

    Authors: , - Automatica, Autom. 2022 cited by 57

  9. NetGO: improving large-scale protein function prediction with massive network information

    Authors: , , , , , , - Nucleic Acids Research, Nucleic Acids Res. 2019 cited by 165

  10. More Quickly-RRT*: Improved Quick Rapidly-exploring Random Tree Star algorithm based on optimized sampling point with better initial solution and convergence rate

    Authors: , , , , - Engineering Applications of Artificial Intelligence, Eng. Appl. Artif. Intell. 2024 cited by 51

  11. Serving Large Language Models on Huawei CloudMatrix384

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Dong Cao, Juncheng Liu, Yongqiang Yang, Xiaolong Bai, Yi Li, Huaguo Xie, Huatao Wu, Zhibin Yu, Lv Chen, Hu Liu, Yujun Ding, Haipei Zhu, Jing Xia, Yi Xiong, Zhou Yu, Heng Liao - ArXiv.org, CoRR 2025 cited by 30

  12. Resveratrol promotes osteogenesis via activating SIRT1/FoxO1 pathway in osteoporosis mice

    Authors: , , , , , - Life Sciences 2020 cited by 124

  13. Fusion PCR and gene targeting in Aspergillus nidulans

    Authors: , , , , , , , - Nature Protocols 2006 cited by 789

  14. Subtype-DCC: decoupled contrastive clustering method for cancer subtype identification based on multi-omics data

    Authors: , , , , , , - Briefings in Bioinformatics, Briefings Bioinform. 2023 cited by 50

  15. RecON: Online learning for sensorless freehand 3D ultrasound reconstruction

    Authors: , , , , , , , , , , , , , - Medical Image Analysis, Medical Image Anal. 2023 cited by 37

  16. Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection

    Authors: , , , , , , , , , , , , - Computational and Structural Biotechnology Journal 2024 cited by 147

  17. Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Junming Xu, Jinghua Yao, Kuan Xu, Kewei Du, Longfei Li, Lei Liang, Lu Yu, Li Tang, Lin Ju, Peng Xu, Qing Cui, Song Liu, Shicheng Li, Shun Song, Song Yan, Tengwei Cai, Tianyi Chen, Ting Guo, Ting Huang, Tao Feng, Tao Wu, Wei Wu, Xiaolu Zhang, Xueming Yang, Xin Zhao, Xiaobo Hu, Xin Lin, Yao Zhao, Yilong Wang, Yongzhen Guo, Yuanyuan Wang, Yue Yang, Yang Cao, Yuhao Fu, Yi Xiong, Yanzhe Li, Zhe Li, Zhiqiang Zhang, Ziqi Liu, Zhaoxin Huan, Zujie Wen, Zhenhang Sun, Zhuoxuan Du, Zhengyu He - ArXiv.org, CoRR 2025 cited by 20

  18. MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph

    Authors: , , , , , , , , , , - Briefings in Bioinformatics, Briefings Bioinform. 2021 cited by 77

  19. Data-driven generative design for mass customization: A case study

    Authors: , , , , - Advanced Engineering Informatics, Adv. Eng. Informatics 2022 cited by 56

  20. Mechanosensitive brain tumor cells construct blood-tumor barrier to mask chemosensitivity

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Lu‐Yang Wang, Chi‐chung Hui, Rinat R. Abzalimov, Ye He, Yu Sun, Xuejun Li, Xi Huang - Neuron 2022 cited by 51

  21. UCSCXenaShiny: an R/CRAN package for interactive analysis of UCSC Xena data

    Authors: , , , , , , , , , , , , , - Bioinformatics, Bioinform. 2021 cited by 246

  22. IOBR: Multi-omics Immuno-Oncology Biological Research to decode tumor microenvironment and signatures

    Authors: , , , , , , , , , , , , - 2020 cited by 78

  23. TEPCAM: Prediction of T‐cell receptor–epitope binding specificity via interpretable deep learning

    Authors: , , , , , , , , , , - Protein Science 2023 cited by 34

  24. Machine learning integrated design for additive manufacturing

    Authors: , , , - Journal of Intelligent Manufacturing, J. Intell. Manuf. 2020 cited by 227