Yongyan Song

1.2k total citations
58 papers, 834 citations indexed

About

Yongyan Song is a scholar working on Molecular Biology, Endocrinology, Diabetes and Metabolism and Cancer Research. According to data from OpenAlex, Yongyan Song has authored 58 papers receiving a total of 834 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 17 papers in Endocrinology, Diabetes and Metabolism and 16 papers in Cancer Research. Recurrent topics in Yongyan Song's work include Lipoproteins and Cardiovascular Health (9 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (9 papers) and Lipid metabolism and disorders (7 papers). Yongyan Song is often cited by papers focused on Lipoproteins and Cardiovascular Health (9 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (9 papers) and Lipid metabolism and disorders (7 papers). Yongyan Song collaborates with scholars based in China, United States and United Kingdom. Yongyan Song's co-authors include Zhan Lü, Qiaozhu Su, Zhi Luo, Muhammad Irfan, Li Zhu, Yi Fang, Yang Yang, Yun Chen, Rituraj Khound and Yang Yang‐Hartwich and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Circulation Research.

In The Last Decade

Yongyan Song

52 papers receiving 818 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yongyan Song China 16 261 221 207 167 167 58 834
Xin Su China 17 227 0.9× 139 0.6× 208 1.0× 163 1.0× 200 1.2× 48 852
L. Maria Belalcazar United States 17 173 0.7× 190 0.9× 174 0.8× 98 0.6× 178 1.1× 34 719
Ryohei Kaseda Japan 17 275 1.1× 213 1.0× 252 1.2× 150 0.9× 98 0.6× 39 1.1k
Kuang‐Chung Shih Taiwan 19 291 1.1× 153 0.7× 330 1.6× 166 1.0× 165 1.0× 53 976
Sang‐Hyun Lee South Korea 20 296 1.1× 116 0.5× 160 0.8× 191 1.1× 180 1.1× 63 973
T Ng Australia 17 259 1.0× 271 1.2× 336 1.6× 217 1.3× 343 2.1× 27 1.0k
Laura Bertoccini Italy 18 198 0.8× 158 0.7× 291 1.4× 108 0.6× 345 2.1× 38 910
Aaron R. Cox United States 20 270 1.0× 298 1.3× 248 1.2× 105 0.6× 139 0.8× 37 955
Yu Hu China 19 453 1.7× 174 0.8× 342 1.7× 243 1.5× 315 1.9× 55 1.4k
Aída Medina-Urrutia Mexico 16 111 0.4× 164 0.7× 252 1.2× 205 1.2× 240 1.4× 56 808

Countries citing papers authored by Yongyan Song

Since Specialization
Citations

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

Fields of papers citing papers by Yongyan Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yongyan Song

This figure shows the co-authorship network connecting the top 25 collaborators of Yongyan Song. A scholar is included among the top collaborators of Yongyan Song 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 Yongyan Song. Yongyan Song 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
1.
Pan, Jia Bao, Xue Wang, Youjin Zhang, et al.. (2025). Associations Between APOC3 and ANGPTL8 Gene Polymorphisms With MASLD Risk and the Mediation Effect of Triglyceride on MASLD in the Chinese Population. Journal of Cellular and Molecular Medicine. 29(7). e70542–e70542. 1 indexed citations
2.
Song, Yongyan, Youjin Zhang, Xinyu Liu, et al.. (2025). The rs7799039 variant in the leptin gene promoter drives insulin resistance through reduced serum leptin levels. Frontiers in Endocrinology. 16. 1589575–1589575.
3.
Song, Yongyan, et al.. (2025). Serum CircCSPP1 is Correlated with the Occurrence and Severity of NAFLD in a Chinese Population. Hormone and Metabolic Research. 57(3). 208–215. 1 indexed citations
4.
Ye, Peng, Dai Zhang, Mingyue Zhang, et al.. (2025). PLK1 inhibitors for the treatment of colorectal cancer. Annals of Medicine and Surgery. 87(7). 4165–4172.
5.
Zhang, Youjin, et al.. (2024). Lipid-lowering effect of Danshen, Fufang Danshen, Shuxuening and Shuxuetong injections: A Systematic Review and Meta-Analysis of Controlled Clinical Trials. The Tohoku Journal of Experimental Medicine. 266(1). 47–58. 3 indexed citations
6.
Wang, Xue, et al.. (2024). Serum CircNIPSNAP3A is Associated with Metabolic Disorders, Atherosclerosis and Severity of Coronary Artery Disease in a Chinese Population. The Tohoku Journal of Experimental Medicine. 263(2). 123–131. 4 indexed citations
7.
8.
Song, Yongyan, et al.. (2022). PPARγ Gene Polymorphisms, Metabolic Disorders, and Coronary Artery Disease. Frontiers in Cardiovascular Medicine. 9. 808929–808929. 17 indexed citations
9.
Song, Yongyan, et al.. (2022). Neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, and monocyte to lymphocyte ratio in depression: A meta-analysis. Journal of Affective Disorders. 308. 375–383. 44 indexed citations
10.
Wang, Hao, Yongyan Song, Yuxin Wu, et al.. (2021). Activation of dsRNA-Dependent Protein Kinase R by miR-378 Sustains Metabolic Inflammation in Hepatic Insulin Resistance. Diabetes. 70(3). 710–719. 15 indexed citations
11.
Luo, Zhi, et al.. (2018). Associations of the PON1 rs854560 polymorphism with plasma lipid levels: a meta-analysis. Lipids in Health and Disease. 17(1). 274–274. 6 indexed citations
12.
Shen, Jing, Neetu Sud, Rituraj Khound, et al.. (2016). Low-Density Lipoprotein Receptor Signaling Mediates the Triglyceride-Lowering Action of Akkermansia muciniphila in Genetic-Induced Hyperlipidemia. Arteriosclerosis Thrombosis and Vascular Biology. 36(7). 1448–1456. 64 indexed citations
13.
Wang, Yanmei, Zhan Lü, Yang Yang‐Hartwich, et al.. (2016). The APOA5 rs662799 polymorphism is associated with dyslipidemia and the severity of coronary heart disease in Chinese women. Lipids in Health and Disease. 15(1). 170–170. 24 indexed citations
14.
Zhu, Li, et al.. (2015). [Advances in the Association between Apolipoprotein (a) Gene Polymorphisms and Coronary Heart Disease].. PubMed. 37(4). 482–8. 5 indexed citations
15.
Song, Yongyan, Yang Yang, Yanmei Wang, et al.. (2015). The apoB100/apoAI ratio is independently associated with the severity of coronary heart disease: a cross sectional study in patients undergoing coronary angiography. Lipids in Health and Disease. 14(1). 150–150. 29 indexed citations
16.
Zhu, Li, et al.. (2015). Lipoprotein ratios are better than conventional lipid parameters in predicting coronary heart disease in Chinese Han people. Kardiologia Polska. 73(10). 931–938. 129 indexed citations
17.
Song, Yongyan, et al.. (2015). Associations of the APOC3 rs5128 polymorphism with plasma APOC3 and lipid levels: a meta-analysis. Lipids in Health and Disease. 14(1). 32–32. 29 indexed citations
18.
Song, Yongyan, Hui Liu, Rongrong Zhang, et al.. (2014). A high-carbohydrate diet lowered blood pressure in healthy Chinese male adolescents. BioScience Trends. 8(2). 132–137. 9 indexed citations
19.
Gyarmati, Péter, et al.. (2013). Chemical fragmentation for massively parallel sequencing library preparation. Journal of Biotechnology. 168(1). 95–100. 4 indexed citations
20.

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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