Sheng‐Da Hsu

6.9k total citations · 3 hit papers
18 papers, 4.0k citations indexed

About

Sheng‐Da Hsu is a scholar working on Molecular Biology, Cancer Research and Immunology. According to data from OpenAlex, Sheng‐Da Hsu has authored 18 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 14 papers in Cancer Research and 3 papers in Immunology. Recurrent topics in Sheng‐Da Hsu's work include MicroRNA in disease regulation (12 papers), Cancer-related molecular mechanisms research (9 papers) and RNA Research and Splicing (7 papers). Sheng‐Da Hsu is often cited by papers focused on MicroRNA in disease regulation (12 papers), Cancer-related molecular mechanisms research (9 papers) and RNA Research and Splicing (7 papers). Sheng‐Da Hsu collaborates with scholars based in Taiwan, United States and Germany. Sheng‐Da Hsu's co-authors include Hsien‐Da Huang, Feng‐Mao Lin, Ann‐Ping Tsou, Hsi‐Yuan Huang, Chih‐Hung Chou, Chih-Min Chiu, Chia-Hung Chien, Sirjana Shrestha, Weiyun Wu and Chi‐Ying F. Huang and has published in prestigious journals such as Nucleic Acids Research, Journal of Clinical Investigation and Journal of Virology.

In The Last Decade

Sheng‐Da Hsu

18 papers receiving 4.0k citations

Hit Papers

miRTarBase: a database curates experimentally validated m... 2010 2026 2015 2020 2010 2013 2012 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sheng‐Da Hsu Taiwan 14 3.2k 2.8k 273 245 174 18 4.0k
Je‐Hyun Yoon United States 31 3.9k 1.2× 3.1k 1.1× 273 1.0× 261 1.1× 85 0.5× 81 4.6k
Shu‐Hao Hsu Taiwan 22 2.0k 0.6× 1.7k 0.6× 398 1.5× 279 1.1× 76 0.4× 58 3.1k
Hsi‐Yuan Huang Taiwan 21 1.7k 0.5× 1.2k 0.4× 142 0.5× 213 0.9× 197 1.1× 64 2.4k
Kun Sun China 32 2.7k 0.9× 2.4k 0.9× 197 0.7× 208 0.8× 440 2.5× 101 4.2k
Christopher F. Bennett United States 13 3.0k 0.9× 2.2k 0.8× 148 0.5× 234 1.0× 106 0.6× 20 3.6k
Rui Zhou China 25 1.8k 0.6× 1.4k 0.5× 129 0.5× 390 1.6× 321 1.8× 86 2.8k
Antonis Kourtidis United States 21 1.7k 0.5× 1.1k 0.4× 155 0.6× 161 0.7× 182 1.0× 41 2.4k
Bilian Jin China 21 1.7k 0.5× 575 0.2× 164 0.6× 204 0.8× 194 1.1× 33 2.4k
Sarmila Majumder United States 28 2.2k 0.7× 1.3k 0.5× 350 1.3× 573 2.3× 148 0.9× 52 3.5k
Li Gong China 28 1.3k 0.4× 669 0.2× 217 0.8× 284 1.2× 273 1.6× 166 2.5k

Countries citing papers authored by Sheng‐Da Hsu

Since Specialization
Citations

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

Fields of papers citing papers by Sheng‐Da Hsu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sheng‐Da Hsu. 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 Sheng‐Da Hsu. The network helps show where Sheng‐Da Hsu may publish in the future.

Co-authorship network of co-authors of Sheng‐Da Hsu

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

All Works

18 of 18 papers shown
1.
Tseng, Ching‐Ping, et al.. (2020). Dual Effects of Let-7b in the Early Stage of Hepatitis C Virus Infection. Journal of Virology. 95(4). 12 indexed citations
2.
Chou, Chih‐Hung, Hsi‐Yuan Huang, Wei-Chih Huang, et al.. (2018). The aquatic animals’ transcriptome resource for comparative functional analysis. BMC Genomics. 19(S2). 103–103. 6 indexed citations
3.
Lee, Ying‐Ray, Hao Zhang, Sheng‐Da Hsu, et al.. (2017). Honeysuckle aqueous extract and induced let-7a suppress dengue virus type 2 replication and pathogenesis. Journal of Ethnopharmacology. 198. 109–121. 34 indexed citations
4.
Shrestha, Sirjana, Chi-Dung Yang, Hsiao-Chin Hong, et al.. (2017). Integrated MicroRNA–mRNA Analysis Reveals miR-204 Inhibits Cell Proliferation in Gastric Cancer by Targeting CKS1B, CXCL1 and GPRC5A. International Journal of Molecular Sciences. 19(1). 87–87. 39 indexed citations
5.
Liu, Yu‐Chen, Sheng‐Da Hsu, Chih‐Hung Chou, et al.. (2016). Transcriptome sequencing based annotation and homologous evidence based scaffolding of Anguilla japonica draft genome. BMC Genomics. 17(S1). 13–13. 7 indexed citations
6.
Liu, Yu‐Chen, Jianrong Li, Chuan-Hu Sun, et al.. (2015). CircNet: a database of circular RNAs derived from transcriptome sequencing data. Nucleic Acids Research. 44(D1). D209–D215. 280 indexed citations
7.
Hsu, Sheng‐Da, Hsi‐Yuan Huang, Chih‐Hung Chou, et al.. (2015). Integrated analyses to reconstruct microRNA-mediated regulatory networks in mouse liver using high-throughput profiling. BMC Genomics. 16(S2). S12–S12. 31 indexed citations
8.
Shrestha, Sirjana, Sheng‐Da Hsu, Hsi‐Yuan Huang, et al.. (2014). A systematic review of microRNA expression profiling studies in human gastric cancer. Cancer Medicine. 3(4). 878–888. 118 indexed citations
9.
Hsu, Sheng‐Da, Hsi‐Yuan Huang, Yiming Sun, et al.. (2014). MethHC: a database of DNA methylation and gene expression in human cancer. Nucleic Acids Research. 43(D1). D856–D861. 233 indexed citations
10.
Chen, Mien‐Cheng, Tzu‐Hao Chang, Sheng‐Da Hsu, et al.. (2014). Unraveling regulatory mechanisms of atrial remodeling of mitral regurgitation pigs by gene expression profiling analysis: role of type I angiotensin II receptor antagonist. Translational research. 165(5). 599–620. 8 indexed citations
11.
Hsu, Sheng‐Da, Yu‐Ting Tseng, Sirjana Shrestha, et al.. (2013). miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Research. 42(D1). D78–D85. 892 indexed citations breakdown →
12.
Chou, Chih‐Hung, Sheng‐Da Hsu, Tzu‐Hao Chang, et al.. (2013). A computational approach for identifying microRNA-target interactions using high-throughput CLIP and PAR-CLIP sequencing. BMC Genomics. 14(S1). S2–S2. 91 indexed citations
13.
Tsai, Wei-Chih, Sheng‐Da Hsu, Tsung‐Ching Lai, et al.. (2012). MicroRNA-122 plays a critical role in liver homeostasis and hepatocarcinogenesis. Journal of Clinical Investigation. 122(8). 2884–2897. 670 indexed citations breakdown →
14.
Cheng, Ju‐Chien, Ching‐Ping Tseng, Sheng‐Da Hsu, et al.. (2012). Let-7b is a novel regulator of hepatitis C virus replication. Cellular and Molecular Life Sciences. 69(15). 2621–2633. 95 indexed citations
15.
Hsu, Justin Bo‐Kai, Chih-Min Chiu, Sheng‐Da Hsu, et al.. (2011). miRTar: an integrated system for identifying miRNA-target interactions in human. BMC Bioinformatics. 12(1). 300–300. 115 indexed citations
16.
Hsu, Sheng‐Da, Feng‐Mao Lin, Weiyun Wu, et al.. (2010). miRTarBase: a database curates experimentally validated microRNA–target interactions. Nucleic Acids Research. 39(suppl_1). D163–D169. 1074 indexed citations breakdown →
17.
Hsu, Sheng‐Da, C. H. Chu, A.-P. Tsou, et al.. (2007). miRNAMap 2.0: genomic maps of microRNAs in metazoan genomes. Nucleic Acids Research. 36(Database). D165–D169. 223 indexed citations
18.
Hsu, Po‐Yuan, Lizhu Lin, Sheng‐Da Hsu, Justin Bo‐Kai Hsu, & Haidong Huang. (2006). ViTa: prediction of host microRNAs targets on viruses. Nucleic Acids Research. 35(Database). D381–D385. 85 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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