Yuning Liu

1.3k citations
71 papers · 943 · h-index 17

Impact in

Papers in

Yuning Liu

65 papers receiving 927 citations

Peers

Yuning Liu
Comparison fields: 5 of 128
  • Biological Psychiatry 144
  • Behavioral Neuroscience 116
  • Neurology 56
  • Neurology 93
  • Epidemiology 193
Replace Susan J. Melhorn with:
Susan J. Melhorn United States
Xinyu Fang China
Yifan Li China
Anne Soop Sweden
M. I. Sarchi Argentina
Evelin Capellari Cárnio Brazil
Seid Muhie United States
Muneeb A. Faiq India
Douglas Hanes United States
Yuning Liu relative to Susan J. Melhorn United States Susan J. Melhorn's profile →
Citations per field
00.5×6.6×
Susan J. Melhorn · 1×
Citations per year

Countries citing papers authored by Yuning Liu

Since Specialization
Citations

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

Fields of papers citing papers by Yuning Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 71 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2012112
2 2015108
3 202175
4 201970
5 201848
6 201945
7 201834
8 201929
9 201925
10 202124
11 202423
12 202121
13 201920
14 202219
15 202019
16 201417
17 201916
18 202215
19 202013
20 202113

About Yuning Liu

Yuning Liu is a scholar working on Cellular and Molecular Neuroscience, Neurology, Epidemiology, Molecular Biology and Genetics, having authored 71 papers that have together received 943 indexed citations. Recurring topics across this work include Parkinson's Disease Mechanisms and Treatments (7 papers), Estrogen and related hormone effects (5 papers), Neurological disorders and treatments (4 papers), Breastfeeding Practices and Influences (4 papers), Neuropeptides and Animal Physiology (4 papers), Child Nutrition and Water Access (3 papers), Impact of Technology on Adolescents (3 papers) and Reproductive System and Pregnancy (3 papers). The work is most often cited by research in Biological Psychiatry (144 citations), Behavioral Neuroscience (116 citations), Neurology (56 citations), Neurology (93 citations) and Epidemiology (193 citations). Yuning Liu has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Kun Tang, Yunli Peng, Chun‐Lei Jiang, Yunxia Wang, Hanyu Wang, Lei Liu, Xia Wang, Song Gao, Fan Zhang and Yuhao Kang. Their work appears in journals such as BMC Public Health, Neurobiology of Disease, Brain Research, American Journal of Physiology-Regulatory, Integrative and Comparative Physiology and The Journal of Steroid Biochemistry and Molecular Biology.

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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