Jiang Long

405 citations
31 papers · 301 · h-index 10

Impact in

Papers in

Jiang Long

25 papers receiving 291 citations

Peers

Jiang Long
Comparison fields: 5 of 54
  • Cancer Research 44
  • Microbiology 12
  • Modeling and Simulation 8
  • Oncology 43
  • Biomaterials 22
Replace Maciej Czerkies with:
Maciej Czerkies Poland
Aditi Mehta Germany
Rabeb Mouna Derbali Canada
Daowei Yang China
Claire Kileztky France
Angela H. M. Kragten Netherlands
Jomkuan Theprungsirikul United States
Jin Lin Zhou China
Can Xiao China
Jiang Long relative to Maciej Czerkies Poland Maciej Czerkies's profile →
Citations per field
00.5×1.5×2×2.4×
Maciej Czerkies · 1×
Citations per year

Countries citing papers authored by Jiang Long

Since Specialization
Citations

This map shows the geographic impact of Jiang Long'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 Jiang Long with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiang Long more than expected).

Fields of papers citing papers by Jiang Long

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jiang Long. 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 Jiang Long. The network helps show where Jiang Long may publish in the future.

Co-authors

The 25 scholars most cited alongside Jiang Long, 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 Jiang Long Line = papers co-authored together Jiang Long links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201158
2 201633
3 201529
4 200728
5 201526
6 201522
7 202215
8 200813
9 199712
10 202412
11
Identification, phylogenetic evolution and expression analysis of GATA transcription factor family in maize (Zea mays).
20209
12 20237
13 20236
14 20236
15 20245
16 20244
17 20233
18 20243
19 20192
20 20242

About Jiang Long

Jiang Long is a scholar working on Epidemiology, Pathology and Forensic Medicine, Oncology, Agronomy and Crop Science and Cancer Research, having authored 31 papers that have together received 301 indexed citations. Recurring topics across this work include Respiratory viral infections research (4 papers), Influenza Virus Research Studies (4 papers), Spine and Intervertebral Disc Pathology (3 papers), Animal Disease Management and Epidemiology (2 papers), Enhanced Recovery After Surgery (2 papers), Text and Document Classification Technologies (2 papers), Rough Sets and Fuzzy Logic (2 papers) and Cancer Genomics and Diagnostics (1 paper). The work is most often cited by research in Cancer Research (44 citations), Microbiology (12 citations), Modeling and Simulation (8 citations), Oncology (43 citations) and Biomaterials (22 citations). Jiang Long has collaborated with scholars based in China, Taiwan and United States. Frequent co-authors include Quanxing Ni, Chen Liu, Guopei Luo, Xianjun Yu, Meng Guo, Shunrong Ji, Jin Xu, Laijun Song, Kaizhou Jin and Zuqiang Liu. Their work appears in journals such as Human Vaccines & Immunotherapeutics, Small, PLoS ONE, Frontiers in Cellular and Infection Microbiology and Arthritis Research & Therapy.

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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