Ruby Yanru Chen-Tsai

432 total citations
10 papers, 309 citations indexed

About

Ruby Yanru Chen-Tsai is a scholar working on Molecular Biology, Genetics and Cellular and Molecular Neuroscience. According to data from OpenAlex, Ruby Yanru Chen-Tsai has authored 10 papers receiving a total of 309 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 6 papers in Genetics and 1 paper in Cellular and Molecular Neuroscience. Recurrent topics in Ruby Yanru Chen-Tsai's work include CRISPR and Genetic Engineering (8 papers), Virus-based gene therapy research (6 papers) and Animal Genetics and Reproduction (3 papers). Ruby Yanru Chen-Tsai is often cited by papers focused on CRISPR and Genetic Engineering (8 papers), Virus-based gene therapy research (6 papers) and Animal Genetics and Reproduction (3 papers). Ruby Yanru Chen-Tsai collaborates with scholars based in China and United States. Ruby Yanru Chen-Tsai's co-authors include Jinxue Ruan, Kui Li, Jie Xu, Qi Zheng, Ruhong Jiang, Ling-Jie Kong, Kui Xu, Tianwen Wu, Shulin Yang and Hegang Li and has published in prestigious journals such as PLoS ONE, Advanced Functional Materials and Cancer Research.

In The Last Decade

Ruby Yanru Chen-Tsai

10 papers receiving 299 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ruby Yanru Chen-Tsai China	7	230	176	36	18	18	10	309
Laura Spector United States	7	302 1.3×	171 1.0×	49 1.4×	12 0.7×	24 1.3×	10	361
Anastasia Emmanouilidou United States	7	203 0.9×	47 0.3×	24 0.7×	10 0.6×	25 1.4×	10	289
Grégoire Cullot France	5	389 1.7×	108 0.6×	40 1.1×	5 0.3×	31 1.7×	10	418
Sabine Knoess Germany	4	522 2.3×	347 2.0×	143 4.0×	13 0.7×	32 1.8×	5	608
Namjin Cho South Korea	8	404 1.8×	119 0.7×	26 0.7×	6 0.3×	49 2.7×	10	436
Yanfei Wen China	8	108 0.5×	128 0.7×	11 0.3×	9 0.5×	21 1.2×	20	322
Peter M. Kutny United States	6	322 1.4×	173 1.0×	9 0.3×	13 0.7×	19 1.1×	7	366
Joost van Haasteren Switzerland	5	233 1.0×	94 0.5×	24 0.7×	6 0.3×	6 0.3×	6	287
Elena Senís Germany	6	292 1.3×	122 0.7×	36 1.0×	11 0.6×	7 0.4×	7	381
Maurice Labuhn Germany	6	269 1.2×	37 0.2×	28 0.8×	13 0.7×	35 1.9×	7	327

Countries citing papers authored by Ruby Yanru Chen-Tsai

Since Specialization

Citations

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

Fields of papers citing papers by Ruby Yanru Chen-Tsai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ruby Yanru Chen-Tsai. 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 Ruby Yanru Chen-Tsai. The network helps show where Ruby Yanru Chen-Tsai may publish in the future.

Co-authorship network of co-authors of Ruby Yanru Chen-Tsai

This figure shows the co-authorship network connecting the top 25 collaborators of Ruby Yanru Chen-Tsai. A scholar is included among the top collaborators of Ruby Yanru Chen-Tsai 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 Ruby Yanru Chen-Tsai. Ruby Yanru Chen-Tsai 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

1.

Zhang, Huimin, Qi Zheng, & Ruby Yanru Chen-Tsai. (2021). Establishment of a Cre-rat resource for creating conditional and physiological relevant models of human diseases. Transgenic Research. 30(1). 91–104. 8 indexed citations

2.

Li, Quan, Guangyan Qi, Xuming Liu, et al.. (2021). Universal Peptide Hydrogel for Scalable Physiological Formation and Bioprinting of 3D Spheroids from Human Induced Pluripotent Stem Cells. Advanced Functional Materials. 31(41). 18 indexed citations

3.

Chen, Hainan, Mi Shi, Avital Gilam, et al.. (2019). Hemophilia A ameliorated in mice by CRISPR-based in vivo genome editing of human Factor VIII. Scientific Reports. 9(1). 16838–16838. 61 indexed citations

4.

Chi, Xiuling, Qi Zheng, Ruhong Jiang, Ruby Yanru Chen-Tsai, & Ling-Jie Kong. (2019). A system for site-specific integration of transgenes in mammalian cells. PLoS ONE. 14(7). e0219842–e0219842. 36 indexed citations

5.

Chen-Tsai, Ruby Yanru. (2018). Using TARGATT™ Technology to Generate Site-Specific Transgenic Mice. Methods in molecular biology. 1874. 71–86. 4 indexed citations

6.

Ruan, Jinxue, Jie Xu, Ruby Yanru Chen-Tsai, & Kui Li. (2017). Genome editing in livestock: Are we ready for a revolution in animal breeding industry?. Transgenic Research. 26(6). 715–726. 70 indexed citations

7.

Jin, Huanyu, Xin Cheng, Nga Thi Thuy Nguyen, et al.. (2017). Abstract 815: Using CRISPR/Cas9 to generate isogenic cell lines and reference standards for applications in cancer diagnostics. Cancer Research. 77(13_Supplement). 815–815. 1 indexed citations

8.

Ruan, Jinxue, Hegang Li, Kui Xu, et al.. (2015). Highly efficient CRISPR/Cas9-mediated transgene knockin at the H11 locus in pigs. Scientific Reports. 5(1). 14253–14253. 97 indexed citations

9.

Diaz, Robert L., et al.. (2014). Comparative analysis of ABCG2+ and ABCG2-retinoblastoma cells in three-dimensional culture. 4. 1–1. 1 indexed citations

10.

Chen-Tsai, Ruby Yanru, et al.. (2013). Genome editing and animal models. Chinese Science Bulletin. 59(1). 1–6. 13 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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