Tabea Moll

3.5k total citations · 1 hit paper
9 papers, 635 citations indexed

About

Tabea Moll is a scholar working on Molecular Biology, Cell Biology and Immunology. According to data from OpenAlex, Tabea Moll has authored 9 papers receiving a total of 635 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 3 papers in Cell Biology and 3 papers in Immunology. Recurrent topics in Tabea Moll's work include vaccines and immunoinformatics approaches (2 papers), Immunotherapy and Immune Responses (2 papers) and Peroxisome Proliferator-Activated Receptors (2 papers). Tabea Moll is often cited by papers focused on vaccines and immunoinformatics approaches (2 papers), Immunotherapy and Immune Responses (2 papers) and Peroxisome Proliferator-Activated Receptors (2 papers). Tabea Moll collaborates with scholars based in United States, Norway and India. Tabea Moll's co-authors include Genevieve M. Boland, Zhi Wei, Dennie T. Frederick, Ryan J. Sullivan, Meenhard Herlyn, Gao Zhang, Benchun Miao, Tian Tian, Keith T. Flaherty and Noam Auslander and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Medicine and Nature Communications.

In The Last Decade

Tabea Moll

7 papers receiving 626 citations

Hit Papers

Robust prediction of response to immune checkpoint blocka... 2018 2026 2020 2023 2018 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tabea Moll United States 7 382 283 226 176 121 9 635
Jinhua Long China 13 268 0.7× 232 0.8× 300 1.3× 286 1.6× 153 1.3× 42 733
Benjamin Van Court United States 11 432 1.1× 348 1.2× 120 0.5× 100 0.6× 65 0.5× 26 636
Sarabjot Pabla United States 15 422 1.1× 251 0.9× 199 0.9× 153 0.9× 179 1.5× 77 720
Jodi L. Kroeger United States 12 510 1.3× 320 1.1× 191 0.8× 83 0.5× 51 0.4× 17 720
Marie-Anne Goyette Canada 11 193 0.5× 179 0.6× 188 0.8× 53 0.3× 64 0.5× 15 457
Victoria L. Bridgeman United Kingdom 9 233 0.6× 156 0.6× 334 1.5× 84 0.5× 132 1.1× 13 626
Min Hwa Shin United States 12 271 0.7× 240 0.8× 193 0.9× 53 0.3× 96 0.8× 19 550
Christo Kole Greece 11 319 0.8× 153 0.5× 277 1.2× 107 0.6× 112 0.9× 17 668
Einav Shoshan United States 9 169 0.4× 197 0.7× 366 1.6× 63 0.4× 119 1.0× 14 567
Trisha R. Sippel United States 11 253 0.7× 422 1.5× 180 0.8× 83 0.5× 94 0.8× 11 687

Countries citing papers authored by Tabea Moll

Since Specialization
Citations

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

Fields of papers citing papers by Tabea Moll

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tabea Moll

This figure shows the co-authorship network connecting the top 25 collaborators of Tabea Moll. A scholar is included among the top collaborators of Tabea Moll 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 Tabea Moll. Tabea Moll 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
1.
Thierer, James H., Ombretta Foresti, Meredith H. Wilson, et al.. (2024). Pla2g12b drives expansion of triglyceride-rich lipoproteins. Nature Communications. 15(1). 2095–2095. 16 indexed citations
2.
Jin, Yang, Eric D. Young, Meng‐Chieh Shen, et al.. (2024). A high-cholesterol zebrafish diet promotes hypercholesterolemia and fasting-associated liver steatosis. Journal of Lipid Research. 65(10). 100637–100637. 6 indexed citations
3.
Moll, Tabea & Steven Farber. (2024). Zebrafish ApoB-Containing Lipoprotein Metabolism: A Closer Look. Arteriosclerosis Thrombosis and Vascular Biology. 44(5). 1053–1064. 7 indexed citations
5.
Wei, Shiyou, Zhi Wei, Dennie T. Frederick, et al.. (2021). Pathway signatures derived from on-treatment tumor specimens predict response to anti-PD1 blockade in metastatic melanoma. Nature Communications. 12(1). 6023–6023. 29 indexed citations
6.
Auslander, Noam, Gao Zhang, Joo Sang Lee, et al.. (2018). Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nature Medicine. 24(10). 1545–1549. 456 indexed citations breakdown →
7.
Tabansky, Inna, Yupu Liang, Maya Frankfurt, et al.. (2018). Molecular profiling of reticular gigantocellularis neurons indicates that eNOS modulates environmentally dependent levels of arousal. Proceedings of the National Academy of Sciences. 115(29). E6900–E6909. 12 indexed citations
8.
Liu, David, Gao Zhang, Alvin Shi, et al.. (2018). Phylogenetic analysis of longitudinal melanoma samples to reveal convergent evolution and markers of immunotherapy resistance.. Journal of Clinical Oncology. 36(15_suppl). 9581–9581.
9.
Dummer, Wolfgang, et al.. (1995). Elevated serum levels of interleukin-10 in patients with metastatic malignant melanoma. Melanoma Research. 5(1). 67–68. 109 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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