Nan Ding

3.8k citations
104 papers · 2.8k · 1 hit paper · h-index 26

Impact in

Papers in

Nan Ding

99 papers receiving 2.7k citations

Nan Ding's Hit Papers

GSA: Genome Sequence Archive 2017 · 494 citations
4940+3+6Years since publication100200300400

Peers

Nan Ding
Comparison fields: 5 of 99
  • Cancer Research 420
  • Molecular Medicine 71
  • Infectious Diseases 170
  • Molecular Biology 469
  • Oncology 128
Replace Dayong Gu with:
Dayong Gu China
Pu Zhang China
Xueqing Zhang China
Pei-Yuan Li China
Seung Yong Hwang South Korea
Wen‐Ching Wang Taiwan
Xiao Liang China
Qixiang Shao China
Hang Fai Kwok Macao
Qianqian Gao China
Nan Ding relative to Dayong Gu China Dayong Gu's profile →
Citations per field
00.5×2×4×5.3×
Dayong Gu · 1×
Citations per year

Countries citing papers authored by Nan Ding

Since Specialization
Citations

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

Fields of papers citing papers by Nan Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 104 papers — load more, or switch the sort, to bring in the rest.

#Work
1
GSA: Genome Sequence Archive
Hit paper breakdown →
2017494
2 2013122
3 2011116
4 201192
5 201586
6 201377
7 201576
8 201674
9 201470
10 201063
11 201963
12 201361
13 201459
14 201957
15 202053
16 201949
17 201845
18 201544
19 201843
20 202142

About Nan Ding

Nan Ding is a scholar working on Cancer Research, Infectious Diseases, Molecular Biology, Molecular Medicine and Pharmacology, having authored 104 papers that have together received 2.8k indexed citations. Recurring topics across this work include MicroRNA in disease regulation (17 papers), SARS-CoV-2 and COVID-19 Research (5 papers), Microbial Natural Products and Biosynthesis (4 papers), Pluripotent Stem Cells Research (3 papers), Effects of Radiation Exposure (3 papers), Cancer Immunotherapy and Biomarkers (2 papers), Inhalation and Respiratory Drug Delivery (2 papers) and Inflammatory Biomarkers in Disease Prognosis (2 papers). The work is most often cited by research in Cancer Research (420 citations), Molecular Medicine (71 citations), Infectious Diseases (170 citations), Molecular Biology (469 citations) and Oncology (128 citations). Nan Ding has collaborated with scholars based in China and United States. Frequent co-authors include Desheng Yao, Junying Chen, Shan Zhao, Hong Chi Zhao, Yujian He, Xiangdong Fang, Zhaofei Pang, Li Li, Cuidan Li and Fei Chen. Their work appears in journals such as Genomics Proteomics & Bioinformatics, Scientific Reports, Oncotarget, RNA Biology and Frontiers in Oncology.

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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