Hao Yang

3.8k citations
144 papers · 2.7k · 1 hit paper · h-index 27

Impact in

Papers in

Hao Yang

134 papers receiving 2.6k citations

Hao Yang's Hit Papers

Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction 2017 · 298 citations
2980+3+6Years since publication50100150200250

Peers

Hao Yang
Comparison fields: 5 of 152
  • Molecular Biology 1.1k
  • Microbiology 95
  • Immunology and Allergy 78
  • Industrial and Manufacturing Engineering 139
  • Information Systems 278
Replace Ni Chen with:
Ni Chen China
Jun S. Wei United States
Xiao‐Feng Sun Sweden
Stephen L. Abrams United States
Jorng‐Tzong Horng Taiwan
Zhimin Gao United States
Satwinder Singh India
Stefan Kolb Switzerland
Xin Ming United States
Hao Yang relative to Ni Chen China Ni Chen's profile →
Citations per field
00.5×7.3×
Ni Chen · 1×
Citations per year

Countries citing papers authored by Hao Yang

Since Specialization
Citations

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

Fields of papers citing papers by Hao Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 144 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction
Hit paper breakdown →
2017298
2 2016164
3 2020112
4 2020102
5 201674
6 200670
7 201667
8 201565
9 201063
10 202063
11 201963
12 202259
13 201853
14 202252
15 202143
16 201942
17 201740
18 201339
19 201738
20 201037

About Hao Yang

Hao Yang is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging, Oncology, Immunology and Spectroscopy, having authored 144 papers that have together received 2.7k indexed citations. Recurring topics across this work include Glycosylation and Glycoproteins Research (21 papers), Monoclonal and Polyclonal Antibodies Research (20 papers), Advanced Proteomics Techniques and Applications (17 papers), RNA Interference and Gene Delivery (11 papers), Cell death mechanisms and regulation (9 papers), Metabolomics and Mass Spectrometry Studies (6 papers), Peptidase Inhibition and Analysis (6 papers) and Nanoparticle-Based Drug Delivery (6 papers). The work is most often cited by research in Molecular Biology (1.1k citations), Microbiology (95 citations), Immunology and Allergy (78 citations), Industrial and Manufacturing Engineering (139 citations) and Information Systems (278 citations). Hao Yang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Xiaofeng Lu, Jingqiu Cheng, Sungyong Seo, Jing Huang, Yan Liu, Lin Wan, Shisheng Wang, Meng Gong, Jingqiu Cheng and Ze Tao. Their work appears in journals such as Applied Microbiology and Biotechnology, Molecular Pharmaceutics, Journal of Proteome Research, Theranostics and Journal of Controlled Release.

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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