Tao Fu

129 total papers · 2.7k total citations
85 papers, 1.8k citations indexed

About

Tao Fu is a scholar working on Molecular Biology, Surgery and Oncology. According to data from OpenAlex, Tao Fu has authored 85 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Molecular Biology, 27 papers in Surgery and 26 papers in Oncology. Recurrent topics in Tao Fu's work include Gastric Cancer Management and Outcomes (13 papers), Cancer-related molecular mechanisms research (7 papers) and Colorectal Cancer Surgical Treatments (7 papers). Tao Fu is often cited by papers focused on Gastric Cancer Management and Outcomes (13 papers), Cancer-related molecular mechanisms research (7 papers) and Colorectal Cancer Surgical Treatments (7 papers). Tao Fu collaborates with scholars based in China, United States and Ethiopia. Tao Fu's co-authors include Zhi-Jiang Zhang, Min Wei, Jingping Yuan, Yu Liu, Xue‐jie Yu, Wei Song, Jun Ren, Can Kong, Weidong Tong and Baohua Liu and has published in prestigious journals such as JAMA, PLoS ONE and Annals of Surgery.

In The Last Decade

Tao Fu

83 papers receiving 1.7k citations

Hit Papers

Novel Coronavirus Infecti... 2020 2026 2022 2024 2020 100 200 300 400

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Tao Fu 599 380 372 294 271 85 1.8k
Ping Wu 604 1.0× 263 0.7× 177 0.5× 115 0.4× 125 0.5× 75 1.5k
Xueqin Chen 459 0.8× 242 0.6× 181 0.5× 424 1.4× 299 1.1× 156 1.8k
Furong Zeng 646 1.1× 528 1.4× 146 0.4× 486 1.7× 349 1.3× 69 1.8k
Guangtong Deng 582 1.0× 522 1.4× 136 0.4× 495 1.7× 347 1.3× 67 1.7k
Ronit Almog 338 0.6× 194 0.5× 522 1.4× 249 0.8× 587 2.2× 68 2.2k
Raj S. Kasthuri 562 0.9× 143 0.4× 302 0.8× 317 1.1× 255 0.9× 70 2.2k
Moslem Bahadori 211 0.4× 275 0.7× 373 1.0× 319 1.1× 266 1.0× 83 1.6k
Jinqiu Yuan 407 0.7× 101 0.3× 592 1.6× 349 1.2× 612 2.3× 97 2.0k
Edward J. Fitzsimons 483 0.8× 148 0.4× 254 0.7× 150 0.5× 262 1.0× 70 1.9k
Vanesa Bijol 807 1.3× 507 1.3× 374 1.0× 433 1.5× 196 0.7× 75 2.3k

Countries citing papers authored by Tao Fu

Since Specialization
Citations

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

Fields of papers citing papers by Tao Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tao Fu

This figure shows the co-authorship network connecting the top 25 collaborators of Tao Fu. A scholar is included among the top collaborators of Tao Fu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tao Fu. Tao Fu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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