Hanlu Gao

769 citations
22 papers · 528 · h-index 11

Impact in

Papers in

Hanlu Gao

21 papers receiving 518 citations

Peers

Hanlu Gao
Comparison fields: 5 of 107
  • Pharmaceutical Science 97
  • Health Informatics 8
  • Drug Discovery 1
  • Computational Theory and Mathematics 92
  • Biomaterials 58
Replace Run Han with:
Run Han Macao
Pauric Bannigan Canada
Zeqing Bao Canada
Zhuyifan Ye Macao
Daniel Rosenkranz Germany
Awwad A. Radwan Saudi Arabia
Popat Kumbhar India
Sahil Kumar India
Qianqian Zhao China
Anna Karagianni Greece
Hanlu Gao relative to Run Han Macao Run Han's profile →
Citations per field
00.5×1.5×
Run Han · 1×
Citations per year

Countries citing papers authored by Hanlu Gao

Since Specialization
Citations

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

Fields of papers citing papers by Hanlu Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2021137
2 2021100
3 202050
4 202140
5 202032
6 201928
7 202126
8 202225
9 202315
10 202015
11 202212
12 201810
13 20179
14 20229
15 20235
16
[Pre-diabetes mellitus influenced by hyperuricemia].
20135
17 20254
18 20202
19 20232
20 20211

About Hanlu Gao

Hanlu Gao is a scholar working on Pharmaceutical Science, Molecular Biology, Oncology, Pathology and Forensic Medicine and Computational Theory and Mathematics, having authored 22 papers that have together received 528 indexed citations. Recurring topics across this work include Drug Solubulity and Delivery Systems (7 papers), Computational Drug Discovery Methods (3 papers), Genetic factors in colorectal cancer (2 papers), Analytical Chemistry and Chromatography (2 papers), Protein purification and stability (2 papers), Gout, Hyperuricemia, Uric Acid (2 papers), Plant-based Medicinal Research (2 papers) and Smoking Behavior and Cessation (1 paper). The work is most often cited by research in Pharmaceutical Science (97 citations), Health Informatics (8 citations), Drug Discovery (1 citation), Computational Theory and Mathematics (92 citations) and Biomaterials (58 citations). Hanlu Gao has collaborated with scholars based in China, Macao and United Kingdom. Frequent co-authors include Defang Ouyang, Zhuyifan Ye, Wei Wang, Jie Dong, Jinzhong Lin, Wenwen Zheng, Junjun Li, Hua Yu, Haifeng Li and Nannan Wang. Their work appears in journals such as Tobacco Induced Diseases, Journal of Pharmaceutical Sciences, International Health, Acta Pharmaceutica Sinica B and International Journal of Biological Macromolecules.

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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