Hua Gao

170 papers receiving 4.5k citations

Hua Gao's Hit Papers

Analyzing Learned Molecular Representations for Property Prediction 2019 · 1.1k citations
1.1k0+2+4Years since publication2505007501000

Peers

Hua Gao
Comparison fields: 5 of 162
  • Computational Theory and Mathematics 1.8k
  • Ophthalmology 308
  • Radiology, Nuclear Medicine and Imaging 586
  • Organic Chemistry 728
  • Pharmacology 201
Replace Bruno O. Villoutreix with:
Bruno O. Villoutreix France
Martin Ståhl Switzerland
Defang Ouyang Macao
Susan L. McGovern United States
Shengyong Yang China
Douglas E. V. Pires Australia
Wu Zhong China
Min Shen United States
Michel A. Cuendet Switzerland
Rituraj Purohit India
Hua Gao relative to Bruno O. Villoutreix France Bruno O. Villoutreix's profile →
Citations per field
00.5×7.5×
Bruno O. Villoutreix · 1×
Citations per year

Countries citing papers authored by Hua Gao

Since Specialization
Citations

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

Fields of papers citing papers by Hua Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 181 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Analyzing Learned Molecular Representations for Property Prediction
Hit paper breakdown →
20191142
2 1999232
3 1999212
4 1996188
5 2001184
6 2019135
7 1997119
8 201992
9 201888
10 201082
11 199873
12 202263
13 199663
14 199860
15 200656
16 200254
17 200852
18 201552
19 201945
20 202135

About Hua Gao

Hua Gao is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology, Molecular Biology, Computational Theory and Mathematics and Public Health, Environmental and Occupational Health, having authored 181 papers that have together received 4.6k indexed citations. Recurring topics across this work include Corneal surgery and disorders (37 papers), Corneal Surgery and Treatments (31 papers), Computational Drug Discovery Methods (24 papers), Ocular Surface and Contact Lens (16 papers), Ocular Infections and Treatments (16 papers), Analytical Chemistry and Chromatography (10 papers), Ophthalmology and Visual Impairment Studies (9 papers) and Synthesis and biological activity (5 papers). The work is most often cited by research in Computational Theory and Mathematics (1.8k citations), Ophthalmology (308 citations), Radiology, Nuclear Medicine and Imaging (586 citations), Organic Chemistry (728 citations) and Pharmacology (201 citations). Hua Gao has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Corwin Hansch, Rajni Garg, Weiyun Shi, Angel Guzmán-Pérez, Connor W. Coley, Andrew Palmer, Kyle Swanson, Miriam Mathea, Volker Settels and Philipp Eiden. Their work appears in journals such as Graefe s Archive for Clinical and Experimental Ophthalmology, Cornea, Chemical Reviews, Pharmaceutical Research and BMC Ophthalmology.

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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