Marc Lake

936 total citations
19 papers, 580 citations indexed

About

Marc Lake is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Immunology. According to data from OpenAlex, Marc Lake has authored 19 papers receiving a total of 580 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 8 papers in Radiology, Nuclear Medicine and Imaging and 6 papers in Immunology. Recurrent topics in Marc Lake's work include Monoclonal and Polyclonal Antibodies Research (8 papers), Ion Channels and Receptors (5 papers) and Immunotherapy and Immune Responses (4 papers). Marc Lake is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (8 papers), Ion Channels and Receptors (5 papers) and Immunotherapy and Immune Responses (4 papers). Marc Lake collaborates with scholars based in United States, United Kingdom and South Korea. Marc Lake's co-authors include Karl A. Walter, Chaohong Sun, Jun Chen, Jing Xu, Donghee Kim, Betty Yao, Dawon Kang, Victoria Scott, Paul L. Richardson and Danying Song and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Nature Communications.

In The Last Decade

Marc Lake

19 papers receiving 571 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marc Lake United States 12 252 244 213 80 76 19 580
Ádám Horváth Hungary 13 85 0.3× 144 0.6× 144 0.7× 30 0.4× 14 0.2× 35 435
Robert M. Nwokonko United States 15 398 1.6× 410 1.7× 498 2.3× 12 0.1× 39 0.5× 19 809
Toan D. Nguyen United States 14 99 0.4× 255 1.0× 33 0.2× 32 0.4× 24 0.3× 23 596
Adriana Sumoza‐Toledo Mexico 10 47 0.2× 224 0.9× 378 1.8× 50 0.6× 13 0.2× 17 714
S. Skerratt United Kingdom 13 103 0.4× 316 1.3× 77 0.4× 31 0.4× 17 0.2× 19 561
Leanne Pedi United States 4 285 1.1× 386 1.6× 366 1.7× 10 0.1× 15 0.2× 4 678
Woo-Young Choi United States 9 69 0.3× 369 1.5× 97 0.5× 29 0.4× 9 0.1× 31 641
Dalia Alansary Germany 17 361 1.4× 483 2.0× 659 3.1× 37 0.5× 8 0.1× 34 1.1k
Pulak Kar India 15 269 1.1× 481 2.0× 405 1.9× 37 0.5× 4 0.1× 29 864
Cunnigaiper D. Bhanumathy United States 10 93 0.4× 409 1.7× 161 0.8× 16 0.2× 6 0.1× 11 668

Countries citing papers authored by Marc Lake

Since Specialization
Citations

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

Fields of papers citing papers by Marc Lake

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marc Lake

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

All Works

19 of 19 papers shown
1.
2.
Barlan, Kari, Marc Lake, Charles Lu, et al.. (2023). Genome-scale functional genomics screening highlights genes impacting protein fucosylation in Chinese hamster ovary cells. SLAS DISCOVERY. 29(1). 52–58. 1 indexed citations
3.
4.
Ye, Shiming, Nicole A. Belmar, Mien Sho, et al.. (2019). A Bispecific Molecule Targeting CD40 and Tumor Antigen Mesothelin Enhances Tumor-Specific Immunity. Cancer Immunology Research. 7(11). 1864–1875. 40 indexed citations
5.
Guo, Jun, Xiao Yu, Xin Lü, et al.. (2019). Empowering therapeutic antibodies with IFN-α for cancer immunotherapy. PLoS ONE. 14(8). e0219829–e0219829. 20 indexed citations
6.
Argiriadi, M.A., Lorenzo Benatuil, David A. Egan, et al.. (2019). CD40/anti-CD40 antibody complexes which illustrate agonist and antagonist structural switches. BMC Molecular and Cell Biology. 20(1). 29–29. 19 indexed citations
7.
Guo, Jun, Xiao Yu, Marc Lake, et al.. (2018). Abstract 2783: Empowering therapeutic monoclonal antibodies with IFN-alpha for cancer immunotherapy. Cancer Research. 78(13_Supplement). 2783–2783. 1 indexed citations
8.
Korepanova, Alla, Kenton L. Longenecker, Steve D. Pratt, et al.. (2017). Fragment-based discovery of a potent NAMPT inhibitor. Bioorganic & Medicinal Chemistry Letters. 28(3). 437–440. 8 indexed citations
9.
Stöckmann, Henning, Viktor Todorović, Paul L. Richardson, et al.. (2017). Cell-Surface Receptor–Ligand Interaction Analysis with Homogeneous Time-Resolved FRET and Metabolic Glycan Engineering: Application to Transmembrane and GPI-Anchored Receptors. Journal of the American Chemical Society. 139(46). 16822–16829. 16 indexed citations
10.
Chen, Jun, Dawon Kang, Jing Xu, et al.. (2013). Species differences and molecular determinant of TRPA1 cold sensitivity. Nature Communications. 4(1). 2501–2501. 195 indexed citations
11.
DiGiammarino, Enrico L., John E. Harlan, Karl A. Walter, et al.. (2011). Ligand association rates to the inner-variable-domain of a dual-variable-domain immunoglobulin are significantly impacted by linker design. mAbs. 3(5). 487–494. 34 indexed citations
12.
Han, Ping, Alla Korepanova, Melissa Vos, et al.. (2011). Development of ELISA to measure TRPV1 protein in rat tissues. Journal of Neuroscience Methods. 200(2). 144–152. 2 indexed citations
13.
Chen, Jun, et al.. (2010). Application of Large-Scale Transient Transfection to Cell-Based Functional Assays for Ion Channels and GPCRs. Methods in enzymology on CD-ROM/Methods in enzymology. 485. 293–309. 3 indexed citations
14.
Korepanova, Alla, Larry R. Solomon, Karl A. Walter, et al.. (2008). Expression and purification of human TRPV1 in baculovirus-infected insect cells for structural studies. Protein Expression and Purification. 65(1). 38–50. 13 indexed citations
15.
Sun, Chaohong, Danying Song, Rachel Davis‐Taber, et al.. (2007). Solution structure and mutational analysis of pituitary adenylate cyclase-activating polypeptide binding to the extracellular domain of PAC1-RS. Proceedings of the National Academy of Sciences. 104(19). 7875–7880. 117 indexed citations
16.
Niforatos, Wende, Xufeng Zhang, Marc Lake, et al.. (2007). Activation of TRPA1 Channels by the Fatty Acid Amide Hydrolase Inhibitor 3′-Carbamoylbiphenyl-3-yl cyclohexylcarbamate (URB597). Molecular Pharmacology. 71(5). 1209–1216. 51 indexed citations
17.
Chen, Jun, Marc Lake, Wende Niforatos, et al.. (2006). Utility of Large-Scale Transiently Transfected Cells for Cell-Based High-Throughput Screens to Identify Transient Receptor Potential Channel A1 (TRPA1) Antagonists. SLAS DISCOVERY. 12(1). 61–69. 26 indexed citations
18.
Lake, Marc, Cynthia L. Williamson, & Robert D. Slocum. (1998). Molecular cloning and characterization of a UDP-glucose-4-epimerase gene (galE) and its expression in pea tissues. Plant Physiology and Biochemistry. 36(8). 555–562. 12 indexed citations
19.
Williamson, Cynthia L., Marc Lake, & Robert D. Slocum. (1996). Isolation and characterization of a cDNA encoding a pea ornithine transcarbamoylase (argF) and comparison with other transcarbamoylases. Plant Molecular Biology. 31(6). 1087–1092. 11 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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