Peng Jiang

Active 1994–2026

1,200
Papers
82,370
Citations
136
h-index
702
i10-index

Citations

Citations per year for Peng Jiang1945: 2 citations1967: 1 citations1987: 1 citations1991: 1 citations1992: 2 citations1993: 1 citations1994: 2 citations1995: 7 citations1996: 7 citations1997: 3 citations1998: 9 citations1999: 24 citations2000: 28 citations2001: 67 citations2002: 35 citations2003: 52 citations2004: 43 citations2005: 43 citations2006: 74 citations2007: 63 citations2008: 80 citations2009: 86 citations2010: 126 citations2011: 157 citations2012: 264 citations2013: 312 citations2014: 336 citations2015: 402 citations2016: 484 citations2017: 508 citations2018: 695 citations2019: 1,552 citations2020: 2,457 citations2021: 3,040 citations2022: 3,147 citations2023: 2,930 citations2024: 4,422 citations2025: 3,215 citations2026: 612 citations2027: 1 citations1946–1966: no citations, so these years are not shown1968–1986: no citations, so these years are not shown1988–1990: no citations, so these years are not shown

Citation sources

Countries

World map of the countries and regions citing this authorChina: 11,209 citing papers, 34.9% of this breakdownUnited States: 5,548 citing papers, 17.3% of this breakdownUnited Kingdom: 1,247 citing papers, 3.9% of this breakdownGermany: 1,056 citing papers, 3.3% of this breakdownIndia: 832 citing papers, 2.6% of this breakdownCanada: 760 citing papers, 2.4% of this breakdownAustralia: 752 citing papers, 2.3% of this breakdownItaly: 723 citing papers, 2.2% of this breakdownSpain: 642 citing papers, 2% of this breakdownSouth Korea: 642 citing papers, 2% of this breakdownFrance: 631 citing papers, 2% of this breakdownJapan: 600 citing papers, 1.9% of this breakdown
0%34.9%Other 23.2%

Fields

  • Biochemistry, Genetics and Molecular Biology26%
  • Medicine23.2%
  • Computer Science18.1%
  • Engineering8.6%
  • Immunology and Microbiology3.8%
  • Agricultural and Biological Sciences2.8%
  • Other17.5%

Topics

  • Cancer Immunotherapy and Biomarkers2.6%
  • Ferroptosis and cancer prognosis2.5%
  • Recommender Systems and Techniques1.8%
  • RNA modifications and cancer1.7%
  • Cancer-related molecular mechanisms research1.6%
  • Immune cells in cancer1.5%
  • Other88.3%

Coauthors

All papers

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  1. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response

    Authors: , , , , , , , , , , , , , , - Nature Medicine 2018 cited by 5,555

  2. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer

    Authors: , , , , , , - International Conference on Information and Knowledge Management, CIKM 2019 cited by 2,281

  3. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy

    Authors: , , , , , , , , , , , , - Genome biology 2016 cited by 2,768

  4. The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , David F. Dinges, Douglas Ebert, Jason I. Feinberg, Jorge Gandara, K. George, John Goutsias, George S. Grills, Alan R. Hargens, Martina Heer, Ryan P. Hillary, Andrew N. Hoofnagle, Vivian Hook, Garrett Jenkinson, Peng Jiang, Ali Keshavarzian, Steven S. Laurie, Brittany Lee‐McMullen, Sarah B. Lumpkins, Matthew MacKay, Mark Maienschein‐Cline, Ari Melnick, Tyler M. Moore, Kiichi Nakahira, Hemal H. Patel, Robert A. Pietrzyk, Varsha Rao, Rintaro Saito, Denis Salins, Jan M. Schilling, Dorothy D. Sears, Caroline Sheridan, Michael B. Stenger, Rakel Tryggvadóttir, Alexander E. Urban, Tomáš Vaisar, Benjamin Van Espen, Jing Zhang, Michael G. Ziegler, Sara R. Zwart, John B. Charles, Craig E. Kundrot, Graham Scott, Susan M. Bailey, Mathias Basner, Andrew P. Feinberg, Stuart M. C. Lee, Christopher E. Mason, Emmanuel Mignot, Brinda K. Rana, Scott M. Smith, M Snyder, Fred W. Turek - Science 2019 cited by 1,138

  5. Large-scale public data reuse to model immunotherapy response and resistance

    Authors: , , , , , , - Genome Medicine 2020 cited by 1,136

  6. KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems

    Authors: , , , , , , , , - International Conference on Information & Knowledge Management, CIKM 2022 cited by 156

  7. KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos

    Authors: , , , , , , , - International Conference on Information & Knowledge Management, CIKM 2022 cited by 128

  8. A major chromatin regulator determines resistance of tumor cells to T cell–mediated killing

    Authors: , , , , , , , , , , , , , , , - Science 2018 cited by 875

  9. Tumors exploit FTO-mediated regulation of glycolytic metabolism to evade immune surveillance

    Authors: , , , , , , , , , , , , , , , , , , , - Cell Metabolism 2021 cited by 351

  10. Personalized re-ranking for recommendation

    Authors: , , , , , , , , , , - Conference on Recommender Systems, RecSys 2019 cited by 190

  11. Therapeutically Increasing MHC-I Expression Potentiates Immune Checkpoint Blockade

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , F. Stephen Hodi, Myles Brown, X. Shirley Liu - Cancer Discovery 2021 cited by 282

  12. Big data in basic and translational cancer research

    Authors: , , , , , - Nature reviews. Cancer 2022 cited by 260

  13. Cancer-cell-derived fumarate suppresses the anti-tumor capacity of CD8+ T cells in the tumor microenvironment

    Authors: , , , , , , , , , , , , , - Cell Metabolism 2023 cited by 158

  14. Systematic investigation of cytokine signaling activity at the tissue and single-cell levels

    Authors: , , , , , , , , , , , - Nature Methods 2021 cited by 257

  15. Asparagine enhances LCK signalling to potentiate CD8+ T-cell activation and anti-tumour responses

    Authors: , , , , , - Nature Cell Biology 2021 cited by 226

  16. Characterization of pectin from grapefruit peel: A comparison of ultrasound-assisted and conventional heating extractions

    Authors: , , , , , , , , - Food Hydrocolloids 2016 cited by 592

  17. Regulation of the pentose phosphate pathway in cancer

    Authors: , , - Protein & Cell 2014 cited by 566

  18. Deep learning Inversion of Seismic Data

    Authors: , , , , , , - IEEE Transactions on Geoscience and Remote Sensing, IEEE Trans. Geosci. Remote. Sens. 2019 cited by 369

  19. Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation

    Authors: , , , , , , , , - SIGIR Conference on Research and Development in Information Retrieval 2023 cited by 62

  20. In vivo CRISPR screens identify the E3 ligase Cop1 as a modulator of macrophage infiltration and cancer immunotherapy target

    Authors: , , , , , , , , , , , , , , , , , , , , , - Cell 2021 cited by 200

  21. Immunostimulatory Cancer-Associated Fibroblast Subpopulations Can Predict Immunotherapy Response in Head and Neck Cancer

    Authors: , , , , , , , , , , , , , , , , , - Clinical Cancer Research 2022 cited by 209

  22. Fibroblast inflammatory priming determines regenerative versus fibrotic skin repair in reindeer

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , John R. Matyas, Jeff Biernaskie - Cell 2022 cited by 172

  23. A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation

    Authors: , , , , , , , , - Conference on Recommender Systems, RecSys 2019 cited by 145

  24. Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation

    Authors: , , , , , , , , - SIGKDD Conference on Knowledge Discovery and Data Mining 2022 cited by 88