Xiaosong Ben

927 citations
45 papers · 478 indexed · h-index 12

Xiaosong Ben

40 papers receiving 471 citations

Peers

Xiaosong Ben
Comparison fields: 5 of 104
  • Modeling and Simulation 96
  • Oncology 107
  • Pulmonary and Respiratory Medicine 119
  • Transportation 22
  • Surgery 125
Replace Dexin Gong with:
Dexin Gong China
Changhoon Kim South Korea
Denise Danos United States
Sung Hwi Hong South Korea
Shiromi M. Perera United States
Guoqiang Du China
Jiyoung Rhee South Korea
Gwang Hun Jeong South Korea
Jon R. Snyder United States
Tengyue Hu China
Xiaosong Ben relative to Dexin Gong China Dexin Gong's profile →
Citations per field
00.5×7.3×
Dexin Gong · 1×
Citations per year

Countries citing papers authored by Xiaosong Ben

Since Specialization
Citations

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

Fields of papers citing papers by Xiaosong Ben

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 20236
4 20232
5 20224
6 20222
7 20225
8 20222
9 20221
10 202110
11 20218
12 20211
13 202119
14 2020133
15 201739
16 201521
17 20152
18 201218
19 201132
20
NCCN Esophageal Neoplasms Practice Guidelines
20081

About Xiaosong Ben

Xiaosong Ben is a scholar working on Pulmonary and Respiratory Medicine, Surgery, Oncology, Developmental Biology and Obstetrics and Gynecology, having authored 45 papers that have together received 478 indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (15 papers), Esophageal Cancer Research and Treatment (11 papers), Lung Cancer Treatments and Mutations (10 papers), Gastric Cancer Management and Outcomes (6 papers), Pancreatic and Hepatic Oncology Research (5 papers), RNA modifications and cancer (5 papers), Pectus Deformity Diagnosis and Treatment (4 papers) and Pregnancy and preeclampsia studies (3 papers). The work is most often cited by research in Modeling and Simulation (96 citations), Oncology (107 citations), Pulmonary and Respiratory Medicine (119 citations), Transportation (22 citations) and Surgery (125 citations). Xiaosong Ben has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Liang Xie, Haiyu Zhou, Ye Xiong, Jessica Steele, Corrine W. Ruktanonchai, Andrew J. Tatem, Adam Sadilek, Andrew Oplinger, Nick Ruktanonchai and Olivia Prosper. Their work appears in journals such as Annals of Translational Medicine, Journal of Clinical Oncology, Journal of Thoracic Oncology, Frontiers in Oncology and Annals of Surgical 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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