Ding Ma

39 papers receiving 394 citations

Peers

Ding Ma
Comparison fields: 5 of 110
  • Developmental Neuroscience 14
  • Cancer Research 48
  • Molecular Biology 209
  • Numerical Analysis 16
  • Cellular and Molecular Neuroscience 40
Replace Jing Tong with:
Jing Tong China
Sebastián Zamorano Chile
Mariya Misheva United Kingdom
Kum-Joo Shin South Korea
Wenjie Wei China
Farrah Zahed Germany
Hong Peng China
Shihe Li China
Assaf D. Rubinstein Israel
Yunfei Zhang China
Ding Ma relative to Jing Tong China Jing Tong's profile →
Citations per field
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Citations per year

Countries citing papers authored by Ding Ma

Since Specialization
Citations

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

Fields of papers citing papers by Ding Ma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 42 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201747
2 200844
3 201637
4 200630
5 201929
6 200729
7 201728
8 201921
9 202219
10 201514
11 202414
12 200610
13 20158
14 20177
15 20087
16 20206
17
Clinicopathological Significance of VEGF-C, VEGFR-3 and Cyclooxygenase-2 in Early-Stage Cervical Cancer.
20086
18 20086
19 20135
20 20174

About Ding Ma

Ding Ma is a scholar working on Molecular Biology, Discrete Mathematics and Combinatorics, Algebra and Number Theory, Numerical Analysis and Oncology, having authored 42 papers that have together received 408 indexed citations. Recurring topics across this work include Advanced Mathematical Identities (5 papers), Analytic Number Theory Research (5 papers), Advanced Combinatorial Mathematics (4 papers), Microbial Metabolic Engineering and Bioproduction (3 papers), Neurogenesis and neuroplasticity mechanisms (2 papers), Advanced Optimization Algorithms Research (2 papers), Endometrial and Cervical Cancer Treatments (2 papers) and Microplastics and Plastic Pollution (2 papers). The work is most often cited by research in Developmental Neuroscience (14 citations), Cancer Research (48 citations), Molecular Biology (209 citations), Numerical Analysis (16 citations) and Cellular and Molecular Neuroscience (40 citations). Ding Ma has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Michael A. Saunders, Laurence Yang, Bernhard Ø. Palsson, Jinlong He, Jun Yang, Ali Ebrahim, Johnny Henderson, Colton J. Lloyd, Yang Liu and Qinyan Yang. Their work appears in journals such as Oncology Reports, The Journal of Comparative Neurology, Biochemical and Biophysical Research Communications, Mathematische Annalen and Applied Physics Letters.

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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