Liling Yang

1.4k citations
34 papers · 1.1k · h-index 17

Impact in

Papers in

Liling Yang

31 papers receiving 1.1k citations

Peers

Liling Yang
Comparison fields: 5 of 114
  • Biological Psychiatry 181
  • Behavioral Neuroscience 236
  • Physiology 426
  • Cellular and Molecular Neuroscience 216
  • Neurology 62
Replace Mónica Tramullas with:
Mónica Tramullas Spain
Liu United States
Lilian Basso France
Xu Chu China
Jolanta Parada-Turska Poland
Frederico Costa Pereira Portugal
Gianmario Frigo Italy
Xu Wu China
Amy A. Gorman United States
Liling Yang relative to Mónica Tramullas Spain Mónica Tramullas's profile →
Citations per field
00.5×2×3×3.6×
Mónica Tramullas · 1×
Citations per year

Countries citing papers authored by Liling Yang

Since Specialization
Citations

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

Fields of papers citing papers by Liling Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2012279
2 2005119
3 2004114
4 202285
5 200867
6 200750
7 200547
8 200542
9 200736
10 202230
11 200829
12 201627
13 201126
14 201526
15 202423
16 202022
17 202421
18 202417
19 200717
20 202514

About Liling Yang

Liling Yang is a scholar working on Physiology, Cellular and Molecular Neuroscience, Plant Science, Neurology and Behavioral Neuroscience, having authored 34 papers that have together received 1.1k indexed citations. Recurring topics across this work include Pain Mechanisms and Treatments (10 papers), Smart Agriculture and AI (6 papers), Leaf Properties and Growth Measurement (4 papers), Neuroscience and Neuropharmacology Research (4 papers), Neuropeptides and Animal Physiology (4 papers), Autoimmune Neurological Disorders and Treatments (3 papers), Greenhouse Technology and Climate Control (2 papers) and Stress Responses and Cortisol (2 papers). The work is most often cited by research in Biological Psychiatry (181 citations), Behavioral Neuroscience (236 citations), Physiology (426 citations), Cellular and Molecular Neuroscience (216 citations) and Neurology (62 citations). Liling Yang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Jianren Mao, Shuxing Wang, Grewo Lim, Backil Sung, Qing Zhong Zeng, Michael F. McCabe, Lucy L Chen, Hyangin Kim, Yinghong Tian and Gabriel Rusanescu. Their work appears in journals such as Pain, Brain Research, Journal of Neuroscience, Neurological Sciences and IEEE Transactions on Image Processing.

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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