Ning Tu

640 citations
26 papers · 469 · h-index 12

Impact in

Papers in

Ning Tu

24 papers receiving 461 citations

Peers

Ning Tu
Comparison fields: 5 of 94
  • Modeling and Simulation 26
  • Oncology 122
  • Pulmonary and Respiratory Medicine 130
  • Cancer Research 55
  • Radiology, Nuclear Medicine and Imaging 83
Replace Vikas Malhotra with:
Vikas Malhotra India
Dainius Characiejus Lithuania
Vishal Jindal United States
Tomáš Kepák Czechia
Michael Asger Andersen Denmark
H. Lepetit France
Matthew K. Stein United States
Carissa Chu United States
Zhuohui Chen China
Ning Tu relative to Vikas Malhotra India Vikas Malhotra's profile →
Citations per field
00.5×3.1×
Vikas Malhotra · 1×
Citations per year

Countries citing papers authored by Ning Tu

Since Specialization
Citations

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

Fields of papers citing papers by Ning Tu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1995133
2 201768
3 202058
4 201824
5 201824
6 202221
7 202119
8 202114
9 202213
10 202213
11 201712
12 202211
13 202111
14 20207
15 20207
16 20196
17 20206
18 20245
19 20245
20
Fast Parallel File Replication in Data Grid
20043

About Ning Tu

Ning Tu is a scholar working on Radiology, Nuclear Medicine and Imaging, Cognitive Neuroscience, Pulmonary and Respiratory Medicine, Clinical Psychology and Neurology, having authored 26 papers that have together received 469 indexed citations. Recurring topics across this work include Advanced Neuroimaging Techniques and Applications (7 papers), Functional Brain Connectivity Studies (7 papers), COVID-19 and Mental Health (3 papers), Long-Term Effects of COVID-19 (2 papers), Prostate Cancer Diagnosis and Treatment (2 papers), Mental Health Research Topics (2 papers), MRI in cancer diagnosis (2 papers) and COVID-19 epidemiological studies (2 papers). The work is most often cited by research in Modeling and Simulation (26 citations), Oncology (122 citations), Pulmonary and Respiratory Medicine (130 citations), Cancer Research (55 citations) and Radiology, Nuclear Medicine and Imaging (83 citations). Ning Tu has collaborated with scholars based in China, United States and Taiwan. Frequent co-authors include Guangyao Wu, Waun Ki Hong, Walter N. Hittelman, J Y Ro, J. Jack Lee, A-R Yoon, Zhi Wen, Jie Zhang, Peng Tang and Ke Wang. Their work appears in journals such as Frontiers in Psychiatry, Scientific Reports, Medicine, Journal of Affective Disorders and American Journal of Roentgenology.

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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