Dianbo Liu

156 total papers · 6.4k total citations
33 papers, 1.1k citations indexed

About

Dianbo Liu is a scholar working on Artificial Intelligence, Health Information Management and Health Informatics. According to data from OpenAlex, Dianbo Liu has authored 33 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 5 papers in Health Information Management and 5 papers in Health Informatics. Recurrent topics in Dianbo Liu's work include Machine Learning in Healthcare (7 papers), Privacy-Preserving Technologies in Data (5 papers) and Artificial Intelligence in Healthcare and Education (5 papers). Dianbo Liu is often cited by papers focused on Machine Learning in Healthcare (7 papers), Privacy-Preserving Technologies in Data (5 papers) and Artificial Intelligence in Healthcare and Education (5 papers). Dianbo Liu collaborates with scholars based in United States, China and Canada. Dianbo Liu's co-authors include Hao Deng, Huang Li, Mauricio Santillana, Canelle Poirier, Kenneth D. Mandl, Yifeng Yin, Shifa Zhang, Timothy A. Miller, Wei Luo and Maimuna S. Majumder and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Dianbo Liu

30 papers receiving 1.0k citations

Hit Papers

Patient clustering improv... 2019 2026 2021 2023 2019 50 100 150 200 250

Author Peers

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

Author Last Decade Papers Cites
Dianbo Liu 498 178 119 92 88 33 1.1k
Jamal Hussain 474 1.0× 217 1.2× 35 0.3× 67 0.7× 278 3.2× 47 1.3k
Ibrahim Gad 341 0.7× 169 0.9× 37 0.3× 96 1.0× 128 1.5× 48 1.1k
Adam Sadilek 333 0.7× 259 1.5× 34 0.3× 96 1.0× 39 0.4× 31 1.4k
Lei Zhang 419 0.8× 342 1.9× 67 0.6× 121 1.3× 283 3.2× 70 1.3k
Dong Zhang 273 0.5× 100 0.6× 45 0.4× 34 0.4× 248 2.8× 71 1.2k
Francisco Sahli Costabal 120 0.2× 328 1.8× 109 0.9× 144 1.6× 72 0.8× 41 1.5k
L. J. Muhammad 394 0.8× 103 0.6× 35 0.3× 33 0.4× 337 3.8× 38 950
Jyotismita Chaki 355 0.7× 101 0.6× 48 0.4× 43 0.5× 199 2.3× 49 1.3k
Guiqing He 110 0.2× 179 1.0× 244 2.1× 52 0.6× 224 2.5× 24 1.4k
Jacob Bien 305 0.6× 35 0.2× 192 1.6× 45 0.5× 31 0.4× 35 954

Countries citing papers authored by Dianbo Liu

Since Specialization
Citations

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

Fields of papers citing papers by Dianbo Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dianbo Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Dianbo Liu. A scholar is included among the top collaborators of Dianbo Liu 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 Dianbo Liu. Dianbo Liu 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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