Laising Yen

1.9k total citations · 1 hit paper
24 papers, 1.4k citations indexed

About

Laising Yen is a scholar working on Molecular Biology, Cancer Research and Cellular and Molecular Neuroscience. According to data from OpenAlex, Laising Yen has authored 24 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 8 papers in Cancer Research and 2 papers in Cellular and Molecular Neuroscience. Recurrent topics in Laising Yen's work include RNA Research and Splicing (11 papers), RNA modifications and cancer (9 papers) and RNA and protein synthesis mechanisms (8 papers). Laising Yen is often cited by papers focused on RNA Research and Splicing (11 papers), RNA modifications and cancer (9 papers) and RNA and protein synthesis mechanisms (8 papers). Laising Yen collaborates with scholars based in United States, China and Taiwan. Laising Yen's co-authors include Sachin Gupta, H. Sunny Sun, Ya‐Chi Lin, Shaw‐Jenq Tsai, Ning Chang, Kuei‐Yang Hsiao, Richard C. Mulligan, K. P. Kannan, Wei Li and Liguo Wang and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Biotechnology.

In The Last Decade

Laising Yen

23 papers receiving 1.4k citations

Hit Papers

Noncoding Effects of Circular RNA CCDC66 Promote Colon Ca... 2017 2026 2020 2023 2017 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Laising Yen United States 16 1.3k 706 105 64 59 24 1.4k
Luke A. Yates United Kingdom 12 871 0.7× 452 0.6× 55 0.5× 126 2.0× 49 0.8× 23 1.1k
Daniel Domínguez United States 15 1.2k 1.0× 241 0.3× 72 0.7× 85 1.3× 25 0.4× 37 1.4k
Vihandha O. Wickramasinghe United Kingdom 14 1.6k 1.2× 708 1.0× 52 0.5× 75 1.2× 71 1.2× 19 1.7k
Kyoko Imoto United States 15 781 0.6× 218 0.3× 92 0.9× 141 2.2× 33 0.6× 20 975
Jan Attig United Kingdom 18 1.5k 1.2× 416 0.6× 61 0.6× 103 1.6× 56 0.9× 27 2.0k
H. Tomas Rube United States 14 810 0.6× 134 0.2× 146 1.4× 125 2.0× 71 1.2× 18 1.1k
Chatarina Larsson Sweden 12 817 0.6× 235 0.3× 75 0.7× 77 1.2× 42 0.7× 19 1.1k
Vladimir P. Bermudez United States 21 1.9k 1.4× 298 0.4× 207 2.0× 475 7.4× 39 0.7× 29 2.0k
Takao Naganuma Japan 14 1.4k 1.1× 796 1.1× 81 0.8× 35 0.5× 9 0.2× 17 1.5k
Federico Lazzaro Italy 19 1.5k 1.2× 308 0.4× 123 1.2× 246 3.8× 17 0.3× 32 1.6k

Countries citing papers authored by Laising Yen

Since Specialization
Citations

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

Fields of papers citing papers by Laising Yen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laising Yen

This figure shows the co-authorship network connecting the top 25 collaborators of Laising Yen. A scholar is included among the top collaborators of Laising Yen 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 Laising Yen. Laising Yen 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.
Luo, Liming, et al.. (2024). Control of mammalian gene expression by modulation of polyA signal cleavage at 5′ UTR. Nature Biotechnology. 42(9). 1454–1466. 20 indexed citations
2.
Gupta, Sachin & Laising Yen. (2022). Chimeric RNA Design Principles for RNA-Mediated Gene Fusion. Cells. 11(6). 1002–1002. 2 indexed citations
3.
Gupta, Sachin, et al.. (2021). RNA-driven JAZF1-SUZ12 gene fusion in human endometrial stromal cells. PLoS Genetics. 17(12). e1009985–e1009985. 6 indexed citations
4.
Chen, Yen‐Ju, Chia-Ying Chen, Te‐Lun Mai, et al.. (2020). Genome-wide, integrative analysis of circular RNA dysregulation and the corresponding circular RNA-microRNA-mRNA regulatory axes in autism. Genome Research. 30(3). 375–391. 51 indexed citations
5.
Yen, Laising, et al.. (2019). Validating Gene Fusion as the Source of Chimeric RNAs. Methods in molecular biology. 2079. 187–207. 2 indexed citations
6.
Chiou, Shyh‐Horng, Tze‐Yi Lin, H. Sunny Sun, et al.. (2018). The role of HGF-MET pathway and CCDC66 cirRNA expression in EGFR resistance and epithelial-to-mesenchymal transition of lung adenocarcinoma cells. Journal of Hematology & Oncology. 11(1). 74–74. 65 indexed citations
7.
Gupta, Sachin, Liming Luo, & Laising Yen. (2018). RNA-mediated gene fusion in mammalian cells. Proceedings of the National Academy of Sciences. 115(52). E12295–E12304. 26 indexed citations
8.
Hsiao, Kuei‐Yang, Ya‐Chi Lin, Sachin Gupta, et al.. (2017). Noncoding Effects of Circular RNA CCDC66 Promote Colon Cancer Growth and Metastasis. Cancer Research. 77(9). 2339–2350. 518 indexed citations breakdown →
9.
Jijakli, Kenan, Basel Khraiwesh, Weiqi Fu, et al.. (2016). The in vitro selection world. Methods. 106. 3–13. 34 indexed citations
10.
Kannan, K. P., et al.. (2015). Aberrant MUC1-TRIM46-KRTCAP2 Chimeric RNAs in High-Grade Serous Ovarian Carcinoma. Cancers. 7(4). 2083–2093. 13 indexed citations
11.
Kannan, K. P., Cristian Coarfa, Kimal Rajapakshe, et al.. (2014). CDKN2D-WDFY2 Is a Cancer-Specific Fusion Gene Recurrent in High-Grade Serous Ovarian Carcinoma. PLoS Genetics. 10(3). e1004216–e1004216. 36 indexed citations
12.
Zhang, Hao, Wan‐Wan Lin, K. P. Kannan, et al.. (2013). Aberrant chimeric RNA GOLM1-MAK10 encoding a secreted fusion protein as a molecular signature for human esophageal squamous cell carcinoma. Oncotarget. 4(11). 2135–2143. 30 indexed citations
13.
Kannan, K. P., Liguo Wang, Jianghua Wang, et al.. (2011). Abstract 4973: Recurrent chimeric RNAs enriched in human prostate cancer identified by deep-sequencing. Cancer Research. 71(8_Supplement). 4973–4973.
14.
Wang, Liguo, et al.. (2010). A Statistical Method for the Detection of Alternative Splicing Using RNA-Seq. PLoS ONE. 5(1). e8529–e8529. 29 indexed citations
15.
Yen, Laising, Brent R. Stockwell, & Richard C. Mulligan. (2009). A Mammalian Cell-Based Assay for Screening Inhibitors of RNA Cleavage. Methods in molecular biology. 540. 335–347. 2 indexed citations
16.
Link, Kristian H., Lixia Guo, Tyler D. Ames, et al.. (2007). Engineering high-speed allosteric hammerhead ribozymes. Biological Chemistry. 388(8). 779–786. 41 indexed citations
18.
Yen, Laising, Jennifer M. Svendsen, Jeng-Shin Lee, et al.. (2004). Exogenous control of mammalian gene expression through modulation of RNA self-cleavage. Nature. 431(7007). 471–476. 210 indexed citations
19.
Yen, Laising, Mirella Gonzalez‐Zulueta, Yan Yuan, et al.. (2001). Reduction of functional N‐methyl‐d‐aspartate receptors in neurons by RNase P‐mediated cleavage of the NR1 mRNA. Journal of Neurochemistry. 76(5). 1386–1394. 8 indexed citations
20.
Yen, Laising, Stephen M. Strittmatter, & Robert G. Kalb. (1999). Sequence-specific cleavage of Huntingtin mRNA by catalytic DNA. Annals of Neurology. 46(3). 366–373. 50 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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