Song-Yan Liao

2.1k citations
47 papers · 1.6k indexed · h-index 24
Topics
Tissue Engineering and Regenerative Medicine (13 papers)Pluripotent Stem Cells Research (10 papers)Neuropeptides and Animal Physiology (7 papers)

In The Last Decade

Song-Yan Liao

45 papers receiving 1.6k citations

Peers

Song-Yan Liao
Comparison fields: 5 of 98
  • Molecular Biology 639
  • Surgery 464
  • Genetics 346
  • Cardiology and Cardiovascular Medicine 328
  • Biomedical Engineering 255
Replace Benedetta Mazzanti with:
Benedetta Mazzanti Italy
Duc M. Hoang Vietnam
Fayez F. Safadi United States
M. L. Chu United States
Sarthak Sinha Canada
Paola Campagnolo United Kingdom
Michael Wöltje Germany
Dar‐Ming Lai Taiwan
Elvira Forte United States
Vineet Agrawal United States
Song-Yan Liao relative to Benedetta Mazzanti Italy Benedetta Mazzanti's profile →
Citations per field
00.5×3.1×
Benedetta Mazzanti · 1×
Citations per year

Countries citing papers authored by Song-Yan Liao

Since Specialization
Citations

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

Fields of papers citing papers by Song-Yan Liao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Song-Yan Liao

This figure shows the co-authorship network connecting the top 25 collaborators of Song-Yan Liao. A scholar is included among the top collaborators of Song-Yan Liao 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 Song-Yan Liao. Song-Yan Liao 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 15
2 10
3 9
4 11
5 97
6 33
7 41
8 8
9 58
10 10
11 211
12 100
13 7
14 20
15 31
16 38
17 24
18 25
19 62
20 11

About Song-Yan Liao

Song-Yan Liao is a scholar working on Cardiology and Cardiovascular Medicine, Genetics and Cellular and Molecular Neuroscience, having authored 47 papers that have together received 1.6k indexed citations. Recurring topics across this work include Tissue Engineering and Regenerative Medicine (13 papers), Pluripotent Stem Cells Research (10 papers) and Neuropeptides and Animal Physiology (7 papers). The work is most often cited by research in Genetics (346 citations), Cardiology and Cardiovascular Medicine (328 citations) and Biomaterials (148 citations). Song-Yan Liao has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include Hung‐Fat Tse, Hung‐Fat Tse, Yuelin Zhang, Qizhou Lian, Si-Jia Sun, Xiaoting Liang, Zhe Zhen, EX Wu, Rui Wei and Wing‐Hon Lai. Their work appears in journals such as Journal of the American College of Cardiology, PLoS ONE and The Journal of Clinical Endocrinology & Metabolism.

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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