Ute Fischer

5.1k total citations
86 papers, 2.7k citations indexed

About

Ute Fischer is a scholar working on Molecular Biology, Immunology and Genetics. According to data from OpenAlex, Ute Fischer has authored 86 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Molecular Biology, 24 papers in Immunology and 23 papers in Genetics. Recurrent topics in Ute Fischer's work include Acute Lymphoblastic Leukemia research (17 papers), Cell death mechanisms and regulation (13 papers) and Immunodeficiency and Autoimmune Disorders (9 papers). Ute Fischer is often cited by papers focused on Acute Lymphoblastic Leukemia research (17 papers), Cell death mechanisms and regulation (13 papers) and Immunodeficiency and Autoimmune Disorders (9 papers). Ute Fischer collaborates with scholars based in Germany, United States and Israel. Ute Fischer's co-authors include Klaus Schulze‐Osthoff, Arndt Borkhardt, Christopher Stroh, Wolfgang A. Schulz, Christof M Kramm, Susanne Frank, Sabine Steffens, Nikolai G. Rainov, Catrin Albrecht and Reiner U. Jänicke and has published in prestigious journals such as Nature Communications, Blood and PLoS ONE.

In The Last Decade

Ute Fischer

80 papers receiving 2.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ute Fischer Germany 31 1.4k 672 473 410 319 86 2.7k
Gwenny M. Fuhler Netherlands 31 1.4k 1.0× 810 1.2× 571 1.2× 348 0.8× 413 1.3× 132 3.1k
Patrick Van Deŕ Smissen Belgium 31 1.9k 1.4× 652 1.0× 298 0.6× 456 1.1× 430 1.3× 69 3.7k
Amos Baruch United States 30 2.2k 1.6× 597 0.9× 649 1.4× 227 0.6× 263 0.8× 60 3.8k
Yasuhiko Ebina Japan 36 1.8k 1.3× 338 0.5× 312 0.7× 550 1.3× 567 1.8× 151 4.4k
Reiko Ito Japan 34 1.5k 1.1× 702 1.0× 775 1.6× 517 1.3× 405 1.3× 126 3.7k
Wenli Liu China 33 1.5k 1.1× 683 1.0× 353 0.7× 180 0.4× 321 1.0× 168 3.2k
John A. Wilkins Canada 37 2.5k 1.8× 1.0k 1.5× 401 0.8× 277 0.7× 411 1.3× 130 5.4k
Krishnaraj Rajalingam Germany 32 2.2k 1.6× 603 0.9× 516 1.1× 197 0.5× 347 1.1× 81 3.1k
Naoko Watanabe Japan 29 1.6k 1.2× 470 0.7× 452 1.0× 247 0.6× 197 0.6× 88 2.9k
Pawan Gupta India 33 1.3k 1.0× 409 0.6× 335 0.7× 219 0.5× 481 1.5× 147 3.0k

Countries citing papers authored by Ute Fischer

Since Specialization
Citations

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

Fields of papers citing papers by Ute Fischer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ute Fischer

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

All Works

20 of 20 papers shown
1.
Rüchel, Nadine, F. Crispi, E. Gratacós, et al.. (2025). A gut instinct for childhood leukemia prevention: microbiome-targeting recommendations aimed at parents and caregivers. Frontiers in Public Health. 12. 1445113–1445113. 2 indexed citations
2.
Lü, Zhe, Haifeng C. Xu, K. P. Schaal, et al.. (2025). Deep transfer learning approach for automated cell death classification reveals novel ferroptosis-inducing agents in subsets of B-ALL. Cell Death and Disease. 16(1). 396–396.
3.
Wagener, Rabea, Ute Fischer, Anna Hoffmann, et al.. (2024). Hyperdiploid acute lymphoblastic leukemia in children with LZTR1 germline variants. HemaSphere. 8(1). e26–e26. 2 indexed citations
4.
Castro‐Barquero, Sara, F. Crispi, L. Youssef, et al.. (2024). Maternal Lifestyle and Prenatal Risk Factors for Childhood Leukemia: A Review of the Existing Evidence. Fetal Diagnosis and Therapy. 51(4). 395–410. 2 indexed citations
5.
Schmidt, J, Sabine Hornhardt, Friederike Erdmann, et al.. (2021). Risk Factors for Childhood Leukemia: Radiation and Beyond. Frontiers in Public Health. 9. 805757–805757. 25 indexed citations
6.
Nebral, Karin, Christoph G. W. Gertzen, Ithamar Ganmore, et al.. (2019). JAK2 p.G571S in B-cell precursor acute lymphoblastic leukemia: a synergizing germline susceptibility. Leukemia. 33(9). 2331–2335. 11 indexed citations
7.
Bomken, Simon, Jutte van der Werff ten Bosch, Andishe Attarbaschi, et al.. (2018). Current Understanding and Future Research Priorities in Malignancy Associated With Inborn Errors of Immunity and DNA Repair Disorders: The Perspective of an Interdisciplinary Working Group. Frontiers in Immunology. 9. 2912–2912. 40 indexed citations
8.
Nabhani, Schafiq, Sebastian Ginzel, Hagit Miskin, et al.. (2015). Deregulation of Fas ligand expression as a novel cause of autoimmune lymphoproliferative syndrome-like disease. Haematologica. 100(9). 1189–1198. 11 indexed citations
9.
Revel‐Vilk, Shoshana, Ute Fischer, Bärbel Keller, et al.. (2015). Autoimmune lymphoproliferative syndrome-like disease in patients with LRBA mutation. Clinical Immunology. 159(1). 84–92. 62 indexed citations
10.
Stepensky, Polina, Anne Rensing‐Ehl, Shoshana Revel‐Vilk, et al.. (2014). Early-onset Evans syndrome, immunodeficiency, and premature immunosenescence associated with tripeptidyl-peptidase II deficiency. Blood. 125(5). 753–761. 57 indexed citations
11.
Niegisch, Günter, et al.. (2014). Limited efficacy of specific HDAC6 inhibition in urothelial cancer cells. Cancer Biology & Therapy. 15(6). 742–757. 40 indexed citations
12.
Emadi‐Baygi, Modjtaba, et al.. (2014). MTDH/AEG-1 contributes to central features of the neoplastic phenotype in bladder cancer. Urologic Oncology Seminars and Original Investigations. 32(5). 670–677. 16 indexed citations
13.
14.
Nikpour, Parvaneh, Seyed Javad Mowla, Seyed Mehdi Jafarnejad, Ute Fischer, & Wolfgang A. Schulz. (2009). Differential effects of Nucleostemin suppression on cell cycle arrest and apoptosis in the bladder cancer cell lines 5637 and SW1710. Cell Proliferation. 42(6). 762–769. 34 indexed citations
15.
Fischer, Ute, et al.. (2007). Cutting-Edge Apoptosis-Based Therapeutics. BioDrugs. 21(5). 273–297. 25 indexed citations
16.
Fischer, Ute, et al.. (2007). Does Caspase Inhibition Promote Clonogenic Tumor Growth?. Cell Cycle. 6(24). 3048–3053. 13 indexed citations
17.
Volkmann, X, Ute Fischer, Mathias Bähr, et al.. (2007). Increased hepatotoxicity of tumor necrosis factor–related apoptosis-inducing ligand in diseased human liver. Hepatology. 46(5). 1498–1508. 110 indexed citations
18.
Fischer, Ute & Klaus Schulze‐Osthoff. (2005). New Approaches and Therapeutics Targeting Apoptosis in Disease. Pharmacological Reviews. 57(2). 187–215. 200 indexed citations
19.
Augstein, Petra, A. Dunger, P Heinke, et al.. (2002). Cell Surface Trafficking of Fas in NIT-1 Cells and Dissection of Surface and Total Fas Expression. Biochemical and Biophysical Research Communications. 290(1). 443–451. 14 indexed citations
20.

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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