Zhen Liang

1.1k citations
26 papers · 765 indexed · 1 hit paper · h-index 15

Zhen Liang

24 papers receiving 745 citations

Hit Papers

Artificial intelligence using a latent diffusion model en...2620252026510152025

Peers

Zhen Liang
Comparison fields: 5 of 113
  • Pharmaceutical Science 112
  • General Dentistry 20
  • Oncology 258
  • Public Health, Environmental and Occupational Health 220
  • Rehabilitation 47
Replace Marc D. Succi with:
Marc D. Succi United States
Lisha Wu China
Wanbo Zhu China
Tim T. Wang United States
Muhammad Hammad Butt Pakistan
Eoin Sheehan Ireland
Anna Schoenbrunner United States
Kalman L. Watsky United States
Giovanni Francesco Pellicanò Italy
Helen Goodyear United Kingdom
Zhen Liang relative to Marc D. Succi United States Marc D. Succi's profile →
Citations per field
00.5×4.4×
Marc D. Succi · 1×
Citations per year

Countries citing papers authored by Zhen Liang

Since Specialization
Citations

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

Fields of papers citing papers by Zhen Liang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3
Artificial intelligence using a latent diffusion model enables the generation of diverse and potent antimicrobial peptidesbreakdown →
202526
4 20247
5 202313
6 202121
7 202131
8 202114
9 20213
10 202026
11 20206
12 202019
13 2020158
14 202053
15 2020163
16 202015
17 20204
18 201817
19 20169
20 201134

About Zhen Liang

Zhen Liang is a scholar working on Pharmaceutical Science, Ophthalmology and Oncology, having authored 26 papers that have together received 765 indexed citations. Recurring topics across this work include COVID-19 and healthcare impacts (8 papers), Ocular Surface and Contact Lens (7 papers), Advanced Drug Delivery Systems (7 papers), COVID-19 Clinical Research Studies (4 papers), Essential Oils and Antimicrobial Activity (3 papers), Advancements in Transdermal Drug Delivery (2 papers), Bone Tissue Engineering Materials (2 papers) and Diabetic Foot Ulcer Assessment and Management (2 papers). The work is most often cited by research in Pharmaceutical Science (112 citations), General Dentistry (20 citations) and Oncology (258 citations). Zhen Liang has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Wilson Wang, Shirley Beng Suat Ooi, Diarmuid Murphy, James Hoi Po Hui, Jingjing Yang, Ping Lü, Jingguo Li, Tianyang Zhou, Lei Han and Siyu He. Their work appears in journals such as The Lancet, Journal of Bone and Joint Surgery and Annals of Surgery.

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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