Dongsheng Tu

38.8k citations
313 papers · 25.5k indexed · 12 hit papers · h-index 64

Dongsheng Tu

293 papers receiving 24.7k citations

Hit Papers

Simpl...98199520262005201510002.0k3.0k4.0k

Peers

Dongsheng Tu
Comparison fields: 5 of 209
  • Oncology 15.6k
  • Cancer Research 5.9k
  • Pulmonary and Respiratory Medicine 8.6k
  • Pathology and Forensic Medicine 4.1k
  • Hepatology 1.4k
Replace Eric J. Feuer with:
Eric J. Feuer United States
Kenneth R. Hess United States
Gary H. Lyman United States
Luigi Mariani Italy
Larry Norton United States
Michael W. Kattan United States
Mithat Gönen United States
Edith A. Perez United States
Richard Simon United States
Pierfranco Conté Italy
Dongsheng Tu relative to Eric J. Feuer United States Eric J. Feuer's profile →
Citations per field
00.5×1.6×
Eric J. Feuer · 1×
Citations per year

Countries citing papers authored by Dongsheng Tu

Since Specialization
Citations

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

Fields of papers citing papers by Dongsheng Tu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20241
2 20243
3 202411
4 202414
5 20230
6 20224
7 202231
8 202112
9 20217
10 202019
11 20204
12 201970
13 201820
14 20175
15 20172
16 20177
17 201678
18 2012220
19 2012105
20 2010117

About Dongsheng Tu

Dongsheng Tu is a scholar working on Oncology, Statistics and Probability and Cancer Research, having authored 313 papers that have together received 25.5k indexed citations. Recurring topics across this work include Colorectal Cancer Treatments and Studies (61 papers), Breast Cancer Treatment Studies (40 papers), Estrogen and related hormone effects (37 papers), Cancer Genomics and Diagnostics (33 papers), Lung Cancer Treatments and Mutations (29 papers), Statistical Methods in Clinical Trials (28 papers), Statistical Methods and Inference (28 papers) and Cancer Treatment and Pharmacology (27 papers). The work is most often cited by research in Oncology (15.6k citations), Cancer Research (5.9k citations) and Pulmonary and Respiratory Medicine (8.6k citations). Dongsheng Tu has collaborated with scholars based in Canada, United States and Australia. Frequent co-authors include Jun Shao, Lois E. Shepherd, Malcolm J. Moore, Derek J. Jonker, Lesley Seymour, Chris J. O’Callaghan, Christos S. Karapetis, Andrea Bezjak, John Zalcberg and Niall C. Tebbutt. Their work appears in journals such as Journal of Clinical Oncology, Annals of Oncology, Breast Cancer Research and Treatment, Clinical Cancer Research and New England Journal of Medicine.

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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