Fabian Schuler

1.1k total citations · 1 hit paper
16 papers, 747 citations indexed

About

Fabian Schuler is a scholar working on Molecular Biology, Oncology and Cell Biology. According to data from OpenAlex, Fabian Schuler has authored 16 papers receiving a total of 747 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 7 papers in Oncology and 7 papers in Cell Biology. Recurrent topics in Fabian Schuler's work include Microtubule and mitosis dynamics (7 papers), Cell death mechanisms and regulation (6 papers) and Cancer-related Molecular Pathways (5 papers). Fabian Schuler is often cited by papers focused on Microtubule and mitosis dynamics (7 papers), Cell death mechanisms and regulation (6 papers) and Cancer-related Molecular Pathways (5 papers). Fabian Schuler collaborates with scholars based in Austria, Germany and United States. Fabian Schuler's co-authors include Freddy Radtke, Andreas Villunger, Simone M. Haag, Muhammet F. Gülen, Lionel Apétoh, Andrea Ablasser, Ute Koch, Andreas Villunger, Luca L. Fava and Valentina C. Sladky and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Genes & Development.

In The Last Decade

Fabian Schuler

14 papers receiving 743 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Signalling strength determines proapoptotic functions of STING

2017 376 citations Muhammet F. Gülen, Ute Koch et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fabian Schuler Austria	11	487	397	167	142	125	16	747
Paula Carroll United Kingdom	3	836 1.7×	919 2.3×	329 2.0×	214 1.5×	100 0.8×	3	1.4k
Sze‐Ling Ng United States	7	611 1.3×	492 1.2×	146 0.9×	187 1.3×	29 0.2×	9	1.1k
Yuliya V. Katlinskaya United States	11	252 0.5×	421 1.1×	257 1.5×	35 0.2×	47 0.4×	19	701
Ruyuan Zhou China	11	339 0.7×	376 0.9×	109 0.7×	105 0.7×	161 1.3×	14	674
Prabhat Kumar Purbey India	14	757 1.6×	298 0.8×	118 0.7×	43 0.3×	32 0.3×	19	1.0k
Pamela Strauch United States	11	255 0.5×	332 0.8×	54 0.3×	43 0.3×	64 0.5×	18	586
Sinéad Flannery Ireland	5	358 0.7×	561 1.4×	129 0.8×	121 0.9×	14 0.1×	10	769
Young-Chul Sung South Korea	10	226 0.5×	365 0.9×	198 1.2×	46 0.3×	108 0.9×	10	662
Leonie Smeenk Netherlands	14	567 1.2×	162 0.4×	271 1.6×	28 0.2×	34 0.3×	19	838
Bijin Au Singapore	9	388 0.8×	270 0.7×	89 0.5×	46 0.3×	22 0.2×	13	577

Countries citing papers authored by Fabian Schuler

Since Specialization

Citations

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

Fields of papers citing papers by Fabian Schuler

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fabian Schuler

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

All Works

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16 of 16 papers shown

Schuler, Fabian, Marko Panić, Konstantin Schutjajew, et al.. (2025). Resolving Dye Embedment in MOF‐5: Multimodal Evidence for Perylene‐Based Guest Encapsulation. Advanced Optical Materials. 13(35).

Schuler, Fabian, Manuel D. Haschka, Mathias Drach, et al.. (2024). Chronic spindle assembly checkpoint activation causes myelosuppression and gastrointestinal atrophy. EMBO Reports. 25(6). 2743–2772.

Schuler, Fabian, Diana C.J. Spierings, Andréa E. Tijhuis, et al.. (2024). Extra centrosomes delay DNA damage–driven tumorigenesis. Science Advances. 10(13). eadk0564–eadk0564. 6 indexed citations

Szabó, Tamás, et al.. (2022). The SKP2 ‐p27 axis defines susceptibility to cell death upon CHK1 inhibition. Molecular Oncology. 16(15). 2771–2787. 4 indexed citations

Schuler, Fabian, et al.. (2019). Checkpoint kinase 1 is essential for fetal and adult hematopoiesis. EMBO Reports. 20(8). e47026–e47026. 19 indexed citations

Kremser, Leopold, Fabian Schuler, Doris Wilflingseder, et al.. (2019). Phosphorylation of Drosophila CENP-A on serine 20 regulates protein turn-over and centromere-specific loading. Nucleic Acids Research. 47(20). 10754–10770. 10 indexed citations

Gülen, Muhammet F., Ute Koch, Simone M. Haag, et al.. (2017). Signalling strength determines proapoptotic functions of STING. Nature Communications. 8(1). 427–427. 376 indexed citations breakdown →

Lindner, Silke E., et al.. (2017). The miR‐15 family reinforces the transition from proliferation to differentiation in pre‐B cells. EMBO Reports. 18(9). 1604–1617. 31 indexed citations

Schuler, Fabian, Silke E. Lindner, Sebastian Herzog, et al.. (2017). Checkpoint kinase 1 is essential for normal B cell development and lymphomagenesis. Nature Communications. 8(1). 1697–1697. 27 indexed citations

10.

Fava, Luca L., Fabian Schuler, Valentina C. Sladky, et al.. (2017). The PIDDosome activates p53 in response to supernumerary centrosomes. Genes & Development. 31(1). 34–45. 144 indexed citations

11.

Sladky, Valentina C., Fabian Schuler, Luca L. Fava, & Andreas Villunger. (2017). The resurrection of the PIDDosome – emerging roles in the DNA-damage response and centrosome surveillance. Journal of Cell Science. 130(22). 3779–3787. 30 indexed citations

12.

Sochalska, Maja, et al.. (2016). MYC selects against reduced BCL2A1/A1 protein expression during B cell lymphomagenesis. Oncogene. 36(15). 2066–2073. 17 indexed citations

13.

Sakamoto, Kazuhito, Kyung Hyun Yoo, Taemook Kim, et al.. (2016). Janus Kinase 1 Is Essential for Inflammatory Cytokine Signaling and Mammary Gland Remodeling. Molecular and Cellular Biology. 36(11). 1673–1690. 28 indexed citations

14.

Schuler, Fabian, Florian Baumgartner, Victoria Klepsch, et al.. (2015). The BH3-only protein BIM contributes to late-stage involution in the mouse mammary gland. Cell Death and Differentiation. 23(1). 41–51. 15 indexed citations

15.

Manzl, Claudia, Luca L. Fava, Gerhard Krumschnabel, et al.. (2013). Death of p53-defective cells triggered by forced mitotic entry in the presence of DNA damage is not uniquely dependent on Caspase-2 or the PIDDosome. Cell Death and Disease. 4(12). e942–e942. 33 indexed citations

16.

Manzl, Claudia, Florian Baumgartner, Lukas Peintner, Fabian Schuler, & Andreas Villunger. (2013). Possible pitfalls investigating cell death responses in genetically engineered mouse models and derived cell lines. Methods. 61(2). 130–137. 7 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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