Hao Yang

3.8k citations
150 papers · 2.8k · 1 hit paper · h-index 27

Impact in

Papers in

Hao Yang

141 papers receiving 2.8k citations

Hao Yang's Hit Papers

Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction 2017 · 335 citations
3350+3+6Years since publication100200300

Peers

Hao Yang
Comparison fields: 5 of 154
  • Microbiology 100
  • Molecular Biology 1.1k
  • Immunology and Allergy 79
  • Information Systems 339
  • Industrial and Manufacturing Engineering 151
Replace Ni Chen with:
Ni Chen China
Stephan Fischer Germany
Jun S. Wei United States
Xiao‐Feng Sun Sweden
Stephen L. Abrams United States
Ming Hao United States
Satwinder Singh India
Jorng‐Tzong Horng Taiwan
Yan Yang China
Hao Yang relative to Ni Chen China Ni Chen's profile →
Citations per field
00.5×7.4×
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 150 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 →
2017335
2 2016175
3 2020113
4 2020103
5 200681
6 201674
7 201668
8 201967
9 201065
10 201565
11 202065
12 202259
13 201855
14 202252
15 201945
16 202144
17 201343
18 201741
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 150 papers that have together received 2.8k 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 Microbiology (100 citations), Molecular Biology (1.1k citations), Immunology and Allergy (79 citations), Information Systems (339 citations) and Industrial and Manufacturing Engineering (151 citations). Hao Yang has collaborated with scholars based in China, United States and Finland. Frequent co-authors include Xiaofeng Lu, Jing Huang, Sungyong Seo, Yan Liu, Jingqiu Cheng, 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, Journal of Controlled Release and Theranostics.

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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