Bin Liang

5.2k citations
127 papers · 3.4k indexed · 1 hit paper · h-index 31
Topics
Adipose Tissue and Metabolism (16 papers)Lipid metabolism and biosynthesis (12 papers)Genetics, Aging, and Longevity in Model Organisms (10 papers)
Partner nations
ChinaUnited StatesFrance

In The Last Decade

Bin Liang

117 papers receiving 3.4k citations

Hit Papers

CD36 facilitates fatty acid uptake by dynamic palmitoylat...2020202620222024202050100150200250

Peers

Bin Liang
Comparison fields: 5 of 141
  • Molecular Biology 1.6k
  • Physiology 664
  • Aging 520
  • Cancer Research 409
  • Epidemiology 408
Replace Alexander A. Soukas with:
Alexander A. Soukas United States
Kazuichi Sakamoto Japan
Simon C. Johnson United States
Xun Huang China
Gph Leung Hong Kong
Naoaki Ishii Japan
Evan G. Williams Switzerland
Antoni Barrientos United States
Viviana Pérez United States
Tobias Eisenberg Austria
Bin Liang relative to Alexander A. Soukas United States Alexander A. Soukas's profile →
Citations per field
00.5×1.5×
Alexander A. Soukas · 1×
Citations per year

Countries citing papers authored by Bin Liang

Since Specialization
Citations

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

Fields of papers citing papers by Bin Liang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bin Liang

This figure shows the co-authorship network connecting the top 25 collaborators of Bin Liang. A scholar is included among the top collaborators of Bin Liang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Bin Liang. Bin Liang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 0
3 10
4 0
5 4
6 2
7 0
8 1
9 6
10 3
11 7
12 7
13 17
14 21
15 3
16 3
17 53
18 123
19 38
20 243

About Bin Liang

Bin Liang is a scholar working on Aging, Biochemistry and Cancer Research, having authored 127 papers that have together received 3.4k indexed citations. Recurring topics across this work include Adipose Tissue and Metabolism (16 papers), Lipid metabolism and biosynthesis (12 papers) and Genetics, Aging, and Longevity in Model Organisms (10 papers). The work is most often cited by research in Aging (520 citations), Biochemistry (304 citations) and Endocrine and Autonomic Systems (218 citations). Bin Liang has collaborated with scholars based in China, United States and France. Frequent co-authors include Jennifer L. Watts, Xiaoju Zou, Linqiang Zhang, Daye Cheng, Yunhui Li, Ming‐Hui Zou, Benoı̂t Viollet, Shuangxi Wang, Yuru Zhang and Xiaoyun Wu. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Nature Communications.

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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