Marissa Mock

804 total citations
12 papers, 569 citations indexed

About

Marissa Mock is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering. According to data from OpenAlex, Marissa Mock has authored 12 papers receiving a total of 569 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 5 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Biomedical Engineering. Recurrent topics in Marissa Mock's work include Monoclonal and Polyclonal Antibodies Research (5 papers), Microfluidic and Capillary Electrophoresis Applications (3 papers) and RNA and protein synthesis mechanisms (3 papers). Marissa Mock is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (5 papers), Microfluidic and Capillary Electrophoresis Applications (3 papers) and RNA and protein synthesis mechanisms (3 papers). Marissa Mock collaborates with scholars based in United States, Netherlands and Morocco. Marissa Mock's co-authors include David A. Tirrell, A. James Link, Paul Schimmel, Kirk Beebe, Isaac S. Carrico, Julie C. Liu, Sarah C. Heilshorn, Suzanne C. Edavettal, Christopher J. Langmead and Christian Franck and has published in prestigious journals such as Nature, Journal of the American Chemical Society and Trends in Pharmacological Sciences.

In The Last Decade

Marissa Mock

12 papers receiving 563 citations

Peers

Marissa Mock

MB

OC

RNMI

BE

BIOMA

Jay Duffner United States

J.S. Josan United States

Mitsuko Maeda Japan

Adrian Fegan United States

Paul Moody United Kingdom

Chad J. Pickens United States

Annika Borrmann Netherlands

Duy Tien Ta Switzerland

Christopher Szent-Györgyi United States

Yuan Tian China

Jay Duffner United States

Marissa Mock

611 ×1.5

MB

112 ×0.7

OC

126 ×1.2

RNMI

67 ×0.8

BE

65 ×1.0

BIOMA

Citations per year, relative to Marissa Mock Marissa Mock (= 1×) peers Jay Duffner

Countries citing papers authored by Marissa Mock

Since Specialization

Citations

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

Fields of papers citing papers by Marissa Mock

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marissa Mock

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

All Works

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12 of 12 papers shown

1.

Makowski, Emily K., Lina Wu, Jie Huang, et al.. (2024). Reduction of monoclonal antibody viscosity using interpretable machine learning. mAbs. 16(1). 2303781–2303781. 16 indexed citations

2.

Mock, Marissa, et al.. (2024). Recent advances in generative biology for biotherapeutic discovery. Trends in Pharmacological Sciences. 45(3). 255–267. 7 indexed citations

3.

Mock, Marissa, Suzanne C. Edavettal, Christopher J. Langmead, & Alan J. Russell. (2023). AI can help to speed up drug discovery — but only if we give it the right data. Nature. 621(7979). 467–470. 33 indexed citations

4.

Belouski, Ed, et al.. (2023). VERITAS: Harnessing the power of nomenclature in biologic discovery. mAbs. 15(1). 2207232–2207232. 2 indexed citations

5.

Jiang, Ruoyu, et al.. (2023). Microfluidic viscometer by acoustic streaming transducers. Lab on a Chip. 23(11). 2577–2585. 5 indexed citations

6.

Liu, Shufang, Sara C. Humphreys, Kevin D. Cook, et al.. (2023). Utility of physiologically based pharmacokinetic modeling to predict inter-antibody variability in monoclonal antibody pharmacokinetics in mice. mAbs. 15(1). 2263926–2263926. 1 indexed citations

7.

Chen, Fuyi, Qiang Xiao, John H. Robinson, et al.. (2021). Expression liabilities in a four‐chain bispecific molecule. Biotechnology and Bioengineering. 118(10). 3744–3759. 7 indexed citations

8.

Jorgolli, Marsela, Aaron Winters, Irwin Chen, et al.. (2019). Nanoscale integration of single cell biologics discovery processes using optofluidic manipulation and monitoring. Biotechnology and Bioengineering. 116(9). 2393–2411. 26 indexed citations

9.

Beebe, Kirk, et al.. (2008). Distinct domains of tRNA synthetase recognize the same base pair. Nature. 451(7174). 90–93. 69 indexed citations

10.

Carrico, Isaac S., Stacey A. Maskarinec, Sarah C. Heilshorn, et al.. (2007). Lithographic Patterning of Photoreactive Cell-Adhesive Proteins. Journal of the American Chemical Society. 129(16). 4874–4875. 89 indexed citations

11.

Mock, Marissa, Thierry Michon, Jan C. M. van Hest, & David A. Tirrell. (2006). Stereoselective Incorporation of an Unsaturated Isoleucine Analogue into a Protein Expressed in E. coli. ChemBioChem. 7(1). 83–87. 14 indexed citations

12.

Link, A. James, Marissa Mock, & David A. Tirrell. (2003). Non-canonical amino acids in protein engineering. Current Opinion in Biotechnology. 14(6). 603–609. 300 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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