Gabriel C. Wu

3.6k total citations · 1 hit paper
9 papers, 1.9k citations indexed

About

Gabriel C. Wu is a scholar working on Molecular Biology, Genetics and Finance. According to data from OpenAlex, Gabriel C. Wu has authored 9 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 3 papers in Genetics and 1 paper in Finance. Recurrent topics in Gabriel C. Wu's work include Viral Infectious Diseases and Gene Expression in Insects (4 papers), CRISPR and Genetic Engineering (2 papers) and Bacterial Genetics and Biotechnology (2 papers). Gabriel C. Wu is often cited by papers focused on Viral Infectious Diseases and Gene Expression in Insects (4 papers), CRISPR and Genetic Engineering (2 papers) and Bacterial Genetics and Biotechnology (2 papers). Gabriel C. Wu collaborates with scholars based in United States, Slovakia and Switzerland. Gabriel C. Wu's co-authors include John E. Dueber, Jay D. Keasling, G. Reza Malmirchegini, Kristala L. J. Prather, Tae Seok Moon, Christopher J. Petzold, J. Christopher Anderson, Adam P. Arkin, Mariana Leguía and Jonathan A. Goler and has published in prestigious journals such as Nature, Nucleic Acids Research and Nature Communications.

In The Last Decade

Gabriel C. Wu

8 papers receiving 1.9k citations

Hit Papers

Synthetic protein scaffolds provide modular control over ... 2009 2026 2014 2020 2009 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gabriel C. Wu United States 7 1.7k 424 299 145 127 9 1.9k
Jon E. Ness United States 13 1.4k 0.8× 280 0.7× 181 0.6× 77 0.5× 108 0.9× 14 1.7k
Jochem Gätgens Germany 15 1.0k 0.6× 118 0.3× 365 1.2× 40 0.3× 57 0.4× 31 1.2k
Chunbo Lou China 19 1.5k 0.9× 342 0.8× 257 0.9× 280 1.9× 82 0.6× 51 1.8k
Ethan A. Mirsky United States 5 1.7k 1.0× 505 1.2× 266 0.9× 92 0.6× 197 1.6× 5 1.9k
Joseph P. Torella United States 13 827 0.5× 169 0.4× 192 0.6× 48 0.3× 62 0.5× 19 1.3k
Ashty S. Karim United States 22 2.3k 1.4× 195 0.5× 566 1.9× 206 1.4× 128 1.0× 44 2.6k
Ohsuk Kwon South Korea 28 1.8k 1.1× 798 1.9× 234 0.8× 51 0.4× 207 1.6× 71 2.4k
Sunil S. Chandran United States 15 1.2k 0.7× 188 0.4× 207 0.7× 268 1.8× 48 0.4× 22 1.4k
Maureen B. Quin United States 18 1.1k 0.7× 170 0.4× 151 0.5× 452 3.1× 156 1.2× 24 1.4k
Xiaozhou Luo China 22 1.2k 0.7× 141 0.3× 214 0.7× 145 1.0× 41 0.3× 72 1.9k

Countries citing papers authored by Gabriel C. Wu

Since Specialization
Citations

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

Fields of papers citing papers by Gabriel C. Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gabriel C. Wu. 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 Gabriel C. Wu. The network helps show where Gabriel C. Wu may publish in the future.

Co-authorship network of co-authors of Gabriel C. Wu

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

All Works

9 of 9 papers shown
2.
Matharu, Zimple, Christine Bee, Flavio Schwarz, et al.. (2021). High-Throughput Surface Plasmon Resonance Biosensors for Identifying Diverse Therapeutic Monoclonal Antibodies. Analytical Chemistry. 93(49). 16474–16480. 11 indexed citations
3.
Wu, Gabriel C., Nai‐Kong V. Cheung, George Georgiou, Edward M. Marcotte, & Gregory C. Ippolito. (2016). Temporal stability and molecular persistence of the bone marrow plasma cell antibody repertoire. Nature Communications. 7(1). 13838–13838. 12 indexed citations
4.
Tenaillon, Olivier, Jeffrey E. Barrick, Noah Ribeck, et al.. (2016). Tempo and mode of genome evolution in a 50,000-generation experiment. Nature. 536(7615). 165–170. 294 indexed citations
5.
Kittleson, Joshua T., Gabriel C. Wu, & J. Christopher Anderson. (2012). Successes and failures in modular genetic engineering. Current Opinion in Chemical Biology. 16(3-4). 329–336. 52 indexed citations
6.
Conrado, Robert, Gabriel C. Wu, Jason T. Boock, et al.. (2011). DNA-guided assembly of biosynthetic pathways promotes improved catalytic efficiency. Nucleic Acids Research. 40(4). 1879–1889. 221 indexed citations
7.
Anderson, J. Christopher, John E. Dueber, Mariana Leguía, et al.. (2010). BglBricks: A flexible standard for biological part assembly. Journal of Biological Engineering. 4(1). 1–1. 335 indexed citations
8.
Dueber, John E., Gabriel C. Wu, G. Reza Malmirchegini, et al.. (2009). Synthetic protein scaffolds provide modular control over metabolic flux. Nature Biotechnology. 27(8). 753–759. 989 indexed citations breakdown →
9.
Anderson, J. Christopher, John E. Dueber, Mariana Leguía, et al.. (2009). BBF RFC 21: BglBricks Assembly Standard. DSpace@MIT (Massachusetts Institute of Technology). 1 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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