Mu‐Sen Liu

455 citations
7 papers · 281 indexed · 1 hit paper · h-index 6
Topics
CRISPR and Genetic Engineering (4 papers)Bacterial Genetics and Biotechnology (3 papers)Advanced biosensing and bioanalysis techniques (3 papers)

In The Last Decade

Mu‐Sen Liu

7 papers receiving 280 citations

Hit Papers

Structural basis for mismatch surveillance by CRISPR–Cas92022202620232024202250100150

Peers

Mu‐Sen Liu
Comparison fields: 5 of 40
  • Molecular Biology 273
  • Genetics 41
  • Business and International Management 32
  • Aging 23
  • Insect Science 17
Replace Grace N. Hibshman with:
Grace N. Hibshman United States
Carolin Schmelas Germany
Oana Pelea United Kingdom
Gregory Gotta United States
Sylvain Éthier Canada
Soh Ishiguro Japan
Christine R. Zheng United States
Nóra Weinhardt Hungary
Adam Caulder United Kingdom
Lucas Kissling Switzerland
Mu‐Sen Liu relative to Grace N. Hibshman United States Grace N. Hibshman's profile →
Citations per field
00.5×
Grace N. Hibshman · 1×
Citations per year

Countries citing papers authored by Mu‐Sen Liu

Since Specialization
Citations

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

Fields of papers citing papers by Mu‐Sen Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mu‐Sen Liu. 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 Mu‐Sen Liu. The network helps show where Mu‐Sen Liu may publish in the future.

Co-authorship network of co-authors of Mu‐Sen Liu

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

All Works

7 of 7 papers shown
#WorkIndexed citations
1 3
2
Structural basis for mismatch surveillance by CRISPR–Cas9breakdown →
175
3 67
4 9
5 10
6 11
7 6

About Mu‐Sen Liu

Mu‐Sen Liu is a scholar working on Endocrinology, Genetics and Molecular Biology, having authored 7 papers that have together received 281 indexed citations. Recurring topics across this work include CRISPR and Genetic Engineering (4 papers), Bacterial Genetics and Biotechnology (3 papers) and Advanced biosensing and bioanalysis techniques (3 papers). The work is most often cited by research in Business and International Management (32 citations), Aging (23 citations) and Molecular Biology (273 citations). Mu‐Sen Liu has collaborated with scholars based in United States, Taiwan and Netherlands. Frequent co-authors include David W. Taylor, Kenneth A. Johnson, Ryan S. McCool, Tyler L. Dangerfield, Jack P. K. Bravo, Grace N. Hibshman, Shanzhong Gong, Ming‐Daw Tsai, Wen‐Jin Wu and Xiaoxia Liu. Their work appears in journals such as Nature, Journal of the American Chemical Society and Nature Communications.

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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