Derek M. Mason

658 total citations
8 papers, 307 citations indexed

About

Derek M. Mason is a scholar working on Molecular Biology, Oncology and Infectious Diseases. According to data from OpenAlex, Derek M. Mason has authored 8 papers receiving a total of 307 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 4 papers in Oncology and 3 papers in Infectious Diseases. Recurrent topics in Derek M. Mason's work include CAR-T cell therapy research (4 papers), SARS-CoV-2 and COVID-19 Research (3 papers) and CRISPR and Genetic Engineering (3 papers). Derek M. Mason is often cited by papers focused on CAR-T cell therapy research (4 papers), SARS-CoV-2 and COVID-19 Research (3 papers) and CRISPR and Genetic Engineering (3 papers). Derek M. Mason collaborates with scholars based in Switzerland, Sweden and United States. Derek M. Mason's co-authors include Sai T. Reddy, Cédric R. Weber, Simon M. Meng, Roy A. Ehling, Bastian Wagner, Simon Friedensohn, Lucia Bonati, Pablo Gaínza, Christian Jordi and Bruno E. Correia and has published in prestigious journals such as Nucleic Acids Research, Frontiers in Immunology and Cell Reports.

In The Last Decade

Derek M. Mason

8 papers receiving 297 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Derek M. Mason Switzerland	6	220	178	65	52	50	8	307
Simon M. Meng Switzerland	3	193 0.9×	154 0.9×	57 0.9×	28 0.5×	37 0.7×	3	247
Rahel Frick United States	11	251 1.1×	261 1.5×	144 2.2×	41 0.8×	48 1.0×	16	462
Roy A. Ehling Switzerland	9	284 1.3×	163 0.9×	89 1.4×	138 2.7×	56 1.1×	13	430
Wai‐Heng Lua Singapore	10	162 0.7×	182 1.0×	83 1.3×	18 0.3×	38 0.8×	14	257
Lilia A. Rabia United States	7	259 1.2×	216 1.2×	87 1.3×	27 0.5×	20 0.4×	8	327
Simon Friedensohn Switzerland	9	343 1.6×	270 1.5×	251 3.9×	47 0.9×	62 1.2×	11	526
Wei‐Li Ling Singapore	9	158 0.7×	166 0.9×	82 1.3×	20 0.4×	20 0.4×	16	243
Emily K. Makowski United States	15	347 1.6×	357 2.0×	83 1.3×	71 1.4×	49 1.0×	22	480
Veronica Juan United States	8	286 1.3×	163 0.9×	63 1.0×	13 0.3×	29 0.6×	9	359
Igor Snapkov Norway	10	260 1.2×	153 0.9×	160 2.5×	16 0.3×	68 1.4×	14	405

Countries citing papers authored by Derek M. Mason

Since Specialization

Citations

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

Fields of papers citing papers by Derek M. Mason

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Derek M. Mason. 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 Derek M. Mason. The network helps show where Derek M. Mason may publish in the future.

Co-authorship network of co-authors of Derek M. Mason

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

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Mason, Derek M. & Sai T. Reddy. (2024). Predicting adaptive immune receptor specificities by machine learning is a data generation problem. Cell Systems. 15(12). 1190–1197. 10 indexed citations

2.

Azour, Lea, Yunan Hu, Jane P. Ko, et al.. (2023). Deep Learning Denoising of Low-Dose Computed Tomography Chest Images: A Quantitative and Qualitative Image Analysis. Journal of Computer Assisted Tomography. 47(2). 212–219. 3 indexed citations

3.

Mason, Derek M., Simon Friedensohn, Cédric R. Weber, et al.. (2021). Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning. Nature Biomedical Engineering. 5(6). 600–612. 174 indexed citations

4.

Ehling, Roy A., Cédric R. Weber, Derek M. Mason, et al.. (2021). SARS-CoV-2 reactive and neutralizing antibodies discovered by single-cell sequencing of plasma cells and mammalian display. Cell Reports. 38(3). 110242–110242. 14 indexed citations

5.

Vazquez-Lombardi, Rodrigo, Alexander Yermanos, Roy A. Ehling, et al.. (2021). A Single-Cell Atlas of Lymphocyte Adaptive Immune Repertoires and Transcriptomes Reveals Age-Related Differences in Convalescent COVID-19 Patients. Frontiers in Immunology. 12. 701085–701085. 29 indexed citations

6.

Neumeier, Daniel, et al.. (2019). Antibody discovery and engineering by enhanced CRISPR-Cas9 integration of variable gene cassette libraries in mammalian cells. mAbs. 11(8). 1367–1380. 22 indexed citations

7.

Mason, Derek M., et al.. (2018). Genome Engineering of Hybridomas to Generate Stable Cell Lines for Antibody Expression. Methods in molecular biology. 1850. 79–111. 5 indexed citations

8.

Mason, Derek M., Cédric R. Weber, Simon M. Meng, et al.. (2018). High-throughput antibody engineering in mammalian cells by CRISPR/Cas9-mediated homology-directed mutagenesis. Nucleic Acids Research. 46(14). 7436–7449. 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.

Explore authors with similar magnitude of impact