Can Ergen

1.9k total citations · 1 hit paper
18 papers, 1.1k citations indexed

About

Can Ergen is a scholar working on Molecular Biology, Immunology and Biophysics. According to data from OpenAlex, Can Ergen has authored 18 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 7 papers in Immunology and 4 papers in Biophysics. Recurrent topics in Can Ergen's work include Single-cell and spatial transcriptomics (6 papers), Immune cells in cancer (5 papers) and Immune Cell Function and Interaction (4 papers). Can Ergen is often cited by papers focused on Single-cell and spatial transcriptomics (6 papers), Immune cells in cancer (5 papers) and Immune Cell Function and Interaction (4 papers). Can Ergen collaborates with scholars based in Germany, United States and Israel. Can Ergen's co-authors include Frank Tacke, Felix Heymann, Christian Trautwein, Tom Luedde, Patricia M. Niemietz, Marlene Kohlhepp, Julia Peusquens, Christian Martin, Olivier Govaere and Oliver Krenkel and has published in prestigious journals such as Science, Nature Genetics and Nature Biotechnology.

In The Last Decade

Can Ergen

17 papers receiving 1.1k citations

Hit Papers

Chemokine (C‐C motif) receptor 2–positive monocytes aggra... 2016 2026 2019 2022 2016 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Can Ergen Germany 9 540 327 319 297 227 18 1.1k
Marlene Kohlhepp Germany 14 457 0.8× 563 1.7× 261 0.8× 775 2.6× 172 0.8× 24 1.4k
Dianyuan Zhao China 10 398 0.7× 187 0.6× 221 0.7× 192 0.6× 126 0.6× 20 776
Anke Liepelt Germany 12 395 0.7× 402 1.2× 292 0.9× 502 1.7× 163 0.7× 15 1.2k
Nirupma Trehanpati India 19 246 0.5× 443 1.4× 279 0.9× 518 1.7× 142 0.6× 70 1.0k
Po–Sung Chu Japan 15 242 0.4× 343 1.0× 154 0.5× 442 1.5× 75 0.3× 42 825
Constantinos P. Zambirinis United States 16 720 1.3× 123 0.4× 466 1.5× 260 0.9× 685 3.0× 26 1.6k
Geneviève Soucy Canada 17 291 0.5× 131 0.4× 118 0.4× 194 0.7× 153 0.7× 37 811
Benjamin Ruf United States 14 298 0.6× 146 0.4× 280 0.9× 192 0.6× 343 1.5× 29 869
Carolina Armengol Spain 18 172 0.3× 181 0.6× 486 1.5× 211 0.7× 172 0.8× 32 940
Xingwang Xie China 16 280 0.5× 211 0.6× 474 1.5× 279 0.9× 267 1.2× 42 1.1k

Countries citing papers authored by Can Ergen

Since Specialization
Citations

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

Fields of papers citing papers by Can Ergen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Can Ergen

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

All Works

18 of 18 papers shown
1.
Boyeau, Pierre, Justin Hong, Adam Gayoso, et al.. (2025). Deep generative modeling of sample-level heterogeneity in single-cell genomics. Nature Methods. 22(11). 2264–2274. 2 indexed citations
2.
Koblan, Luke W., Kathryn E. Yost, Pu Zheng, et al.. (2025). High-resolution spatial mapping of cell state and lineage dynamics in vivo with PEtracer. Science. 390(6770). eadx3800–eadx3800. 3 indexed citations
3.
Ergen, Can, et al.. (2025). Scvi-hub: an actionable repository for model-driven single-cell analysis. Nature Methods. 22(9). 1836–1845.
4.
Ergen, Can, Galen Xing, Chenling Xu, et al.. (2024). Consensus prediction of cell type labels in single-cell data with popV. Nature Genetics. 56(12). 2731–2738. 5 indexed citations
5.
Hausmann, Fabian, Can Ergen, Mohamed Marouf, et al.. (2023). DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection. Genome biology. 24(1). 212–212. 8 indexed citations
6.
Steier, Zoë, Laura L. McIntyre, Lydia K. Lutes, et al.. (2023). Single-cell multiomic analysis of thymocyte development reveals drivers of CD4+ T cell and CD8+ T cell lineage commitment. Nature Immunology. 24(9). 1579–1590. 28 indexed citations
7.
Lopez, Romain, Baoguo Li, Hadas Keren‐Shaul, et al.. (2022). DestVI identifies continuums of cell types in spatial transcriptomics data. Nature Biotechnology. 40(9). 1360–1369. 118 indexed citations
8.
Ergen, Can, Patricia M. Niemietz, Felix Heymann, et al.. (2019). Liver fibrosis affects the targeting properties of drug delivery systems to macrophage subsets in vivo. Biomaterials. 206. 49–60. 20 indexed citations
9.
Piecha, Felix, Sabine Jordan, Can Ergen, et al.. (2019). Transjugular Intrahepatic Portosystemic Shunt: A Possible Risk Factor for Direct‐Acting Antiviral Treatment Failure in Patients With Hepatitis C?. Hepatology Communications. 3(5). 614–619. 4 indexed citations
10.
Bartneck, Matthias, Diana Möckel, Olivier Govaere, et al.. (2018). The CCR2+ Macrophage Subset Promotes Pathogenic Angiogenesis for Tumor Vascularization in Fibrotic Livers. Cellular and Molecular Gastroenterology and Hepatology. 7(2). 371–390. 86 indexed citations
11.
Warzecha, Klaudia Theresa, Matthias Bartneck, Diana Möckel, et al.. (2018). Targeting and Modulation of Liver Myeloid Immune Cells by Hard‐Shell Microbubbles. Advanced Biosystems. 2(5). 6 indexed citations
12.
Ergen, Can, Felix Heymann, Wa’el Al Rawashdeh, et al.. (2016). Targeting distinct myeloid cell populations in vivo using polymers, liposomes and microbubbles. Biomaterials. 114. 106–120. 65 indexed citations
13.
Mossanen, Jana C., Oliver Krenkel, Can Ergen, et al.. (2016). Chemokine (C‐C motif) receptor 2–positive monocytes aggravate the early phase of acetaminophen‐induced acute liver injury. Hepatology. 64(5). 1667–1682. 262 indexed citations breakdown →
14.
Overmeire, Eva Van, Benoı̂t Stijlemans, Felix Heymann, et al.. (2015). M-CSF and GM-CSF Receptor Signaling Differentially Regulate Monocyte Maturation and Macrophage Polarization in the Tumor Microenvironment. Cancer Research. 76(1). 35–42. 191 indexed citations
15.
Heymann, Felix, Patricia M. Niemietz, Julia Peusquens, et al.. (2015). Long Term Intravital Multiphoton Microscopy Imaging of Immune Cells in Healthy and Diseased Liver Using CXCR6.Gfp Reporter Mice. Journal of Visualized Experiments. 25 indexed citations
16.
Heymann, Felix, Julia Peusquens, Isis Ludwig‐Portugall, et al.. (2015). Liver inflammation abrogates immunological tolerance induced by Kupffer cells. Hepatology. 62(1). 279–291. 304 indexed citations
17.
Krenkel, Oliver, Jana C. Mossanen, Can Ergen, et al.. (2015). O033 : CCR2+ infiltrating monocytes promote acetaminophen-induced acute liver injury – therapeutic implications of inhibiting CCR2 and CCL2. Journal of Hepatology. 62. S206–S206. 2 indexed citations
18.
Heymann, Felix, Patricia M. Niemietz, Julia Peusquens, et al.. (2015). Long Term Intravital Multiphoton Microscopy Imaging of Immune Cells in Healthy and Diseased Liver Using CXCR6.Gfp Reporter Mice. Journal of Visualized Experiments. 5 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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