Jun Shi

2.5k citations
114 papers · 1.7k · h-index 22

Impact in

  • Hematology top 2%
    • Hematopoietic Stem Cell Transplantation
    • Acute Myeloid Leukemia Research
  • Genetics top 5%
    • Myeloproliferative Neoplasms: Diagnosis and Treatment
    • Mesenchymal stem cell research

Papers in

    • Hematopoietic Stem Cell Transplantation 27
    • Acute Myeloid Leukemia Research 21
    • Myeloproliferative Neoplasms: Diagnosis and Treatment 8
    • Hemoglobinopathies and Related Disorders 7

Jun Shi

107 papers receiving 1.7k citations

Peers

Jun Shi
Comparison fields: 5 of 98
  • Hematology 667
  • Genetics 335
  • Immunology 319
  • Cancer Research 198
  • Molecular Biology 615
Replace Aravind Ramakrishnan with:
Aravind Ramakrishnan United States
Monika Jansson Sweden
Kaavya Paruchuri United States
Amy E. Geddis United States
George Grigoriadis Australia
Yacine Boulaftali France
Changgeng Ruan China
Ken Toba Japan
Imke C.A. Munnix Netherlands
Keren Borensztajn Netherlands
Jun Shi relative to Aravind Ramakrishnan United States Aravind Ramakrishnan's profile →
Citations per field
00.5×1.7×
Aravind Ramakrishnan · 1×
Citations per year

Countries citing papers authored by Jun Shi

Since Specialization
Citations

This map shows the geographic impact of Jun Shi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jun Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Shi more than expected).

Fields of papers citing papers by Jun Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun Shi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jun Shi. The network helps show where Jun Shi may publish in the future.

Co-authors

The 25 scholars most cited alongside Jun Shi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun Shi Line = papers co-authored together Jun Shi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 114 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019174
2 2014163
3 2012109
4 201564
5 201158
6 200758
7 202147
8 201342
9 201541
10 200737
11 201532
12 201730
13 202028
14 202128
15 202127
16 201326
17 201926
18 201725
19 202025
20 201725

About Jun Shi

Jun Shi is a scholar working on Hematology, Genetics, Immunology, Cancer Research and Molecular Biology, having authored 114 papers that have together received 1.7k indexed citations. Recurring topics across this work include Hematopoietic Stem Cell Transplantation (27 papers), Acute Myeloid Leukemia Research (21 papers), Immune Cell Function and Interaction (10 papers), T-cell and B-cell Immunology (9 papers), Myeloproliferative Neoplasms: Diagnosis and Treatment (8 papers), Epigenetics and DNA Methylation (7 papers), Hemoglobinopathies and Related Disorders (7 papers) and Complement system in diseases (5 papers). The work is most often cited by research in Hematology (667 citations), Genetics (335 citations), Immunology (319 citations), Cancer Research (198 citations) and Molecular Biology (615 citations). Jun Shi has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Bei Xu, Hong Wang, Yizhou Zheng, Meili Ge, Xingxin Li, Yingqi Shao, Neng Nie, Jinbo Huang, Zhendong Huang and Shihong Lu. Their work appears in journals such as Annals of Hematology, Blood, Leukemia, Scientific Reports and European Journal Of Haematology.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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