Yu-Ling Yin

436 total citations
10 papers, 274 citations indexed

About

Yu-Ling Yin is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Yu-Ling Yin has authored 10 papers receiving a total of 274 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 6 papers in Cellular and Molecular Neuroscience and 3 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Yu-Ling Yin's work include Receptor Mechanisms and Signaling (5 papers), Neuropeptides and Animal Physiology (4 papers) and Hypothalamic control of reproductive hormones (2 papers). Yu-Ling Yin is often cited by papers focused on Receptor Mechanisms and Signaling (5 papers), Neuropeptides and Animal Physiology (4 papers) and Hypothalamic control of reproductive hormones (2 papers). Yu-Ling Yin collaborates with scholars based in China, India and Slovenia. Yu-Ling Yin's co-authors include Yi Jiang, H. Eric Xu, Wanchao Yin, Xiaoxi Wang, Parker W. de Waal, Zhihai Li, Jing Gao, Hu Zhou, Xiang Gao and Yan Zhang and has published in prestigious journals such as Nature, Nature Communications and Nature Structural & Molecular Biology.

In The Last Decade

Yu-Ling Yin

8 papers receiving 273 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Yu-Ling Yin China	6	217	131	35	32	30	10	274
Yanting Yin United States	8	348 1.6×	196 1.5×	42 1.2×	9 0.3×	38 1.3×	11	379
Heng B. See Australia	11	346 1.6×	139 1.1×	13 0.4×	27 0.8×	28 0.9×	14	410
Sebastian Schneider Germany	9	297 1.4×	133 1.0×	27 0.8×	8 0.3×	70 2.3×	16	366
William Gowen-MacDonald United States	5	343 1.6×	232 1.8×	12 0.3×	15 0.5×	61 2.0×	6	381
Patrice Leclerc Canada	9	237 1.1×	104 0.8×	23 0.7×	86 2.7×	10 0.3×	12	349
Ee Von Moo Denmark	8	283 1.3×	145 1.1×	15 0.4×	5 0.2×	65 2.2×	13	340
Raphael S. Haider Germany	7	195 0.9×	104 0.8×	33 0.9×	6 0.2×	22 0.7×	16	235
Takanobu Takezako Japan	9	248 1.1×	107 0.8×	8 0.2×	111 3.5×	32 1.1×	10	375
Ziva Vuckovic United Kingdom	7	262 1.2×	159 1.2×	46 1.3×	6 0.2×	29 1.0×	8	288
Tobias Kiechle Germany	7	165 0.8×	72 0.5×	15 0.4×	8 0.3×	27 0.9×	11	347

Countries citing papers authored by Yu-Ling Yin

Since Specialization

Citations

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

Fields of papers citing papers by Yu-Ling Yin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yu-Ling Yin. 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 Yu-Ling Yin. The network helps show where Yu-Ling Yin may publish in the future.

Co-authorship network of co-authors of Yu-Ling Yin

This figure shows the co-authorship network connecting the top 25 collaborators of Yu-Ling Yin. A scholar is included among the top collaborators of Yu-Ling Yin 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 Yu-Ling Yin. Yu-Ling Yin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

Liu, Hongli, Chongzhao You, Yixiao Zhang, et al.. (2025). Structural insights into antagonist recognition by the vasopressin V2 receptor. Nature Communications. 16(1). 9734–9734.

Li, Xinzhu, Wen Hu, Kaichun Wu, et al.. (2025). Structural basis of peptide recognition and modulation for neuropeptide FF receptors. Cell Reports. 44(9). 116160–116160.

Guo, Shimeng, Youwei Xu, Xin Li, et al.. (2024). Structural basis for recognition of 26RFa by the pyroglutamylated RFamide peptide receptor. Cell Discovery. 10(1). 58–58. 3 indexed citations

Yin, Yu-Ling, Antao Dai, Tianwei Zhang, et al.. (2024). Dimerization and antidepressant recognition at noradrenaline transporter. Nature. 630(8015). 247–254. 18 indexed citations

Yin, Yu-Ling, et al.. (2024). Insulin Resistance Surrogates May Predict HTN-HUA in Young, Non-Obese Individuals. Diabetes Metabolic Syndrome and Obesity. Volume 17. 3593–3601. 3 indexed citations

Yin, Yu-Ling, Chenyu Ye, Fulai Zhou, et al.. (2021). Molecular basis for kinin selectivity and activation of the human bradykinin receptors. Nature Structural & Molecular Biology. 28(9). 755–761. 36 indexed citations

Yin, Wanchao, Zhihai Li, Mingliang Jin, et al.. (2019). A complex structure of arrestin-2 bound to a G protein-coupled receptor. Cell Research. 29(12). 971–983. 148 indexed citations

Yin, Yu-Ling, Rong Guo, Miao Chen, et al.. (2019). Pretreatment with resveratrol ameliorate trigeminal neuralgia by suppressing matrix metalloproteinase-9/2 in trigeminal ganglion. International Immunopharmacology. 72. 339–347. 22 indexed citations

Liu, Ping, Yu-Ling Yin, Ting Wang, et al.. (2019). Ligand-induced activation of ERK1/2 signaling by constitutively active Gs-coupled 5-HT receptors. Acta Pharmacologica Sinica. 40(9). 1157–1167. 17 indexed citations

10.

Ma, Guanghui, Yan Ye, Dan Zhang, et al.. (2016). Identification and biochemical characterization of DC07090 as a novel potent small molecule inhibitor against human enterovirus 71 3C protease by structure-based virtual screening. European Journal of Medicinal Chemistry. 124. 981–991. 27 indexed citations

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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