Michael Bockmayr

2.6k total citations
35 papers, 1.4k citations indexed

About

Michael Bockmayr is a scholar working on Cancer Research, Molecular Biology and Oncology. According to data from OpenAlex, Michael Bockmayr has authored 35 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Cancer Research, 12 papers in Molecular Biology and 11 papers in Oncology. Recurrent topics in Michael Bockmayr's work include Cancer Genomics and Diagnostics (11 papers), Glioma Diagnosis and Treatment (7 papers) and Radiomics and Machine Learning in Medical Imaging (6 papers). Michael Bockmayr is often cited by papers focused on Cancer Genomics and Diagnostics (11 papers), Glioma Diagnosis and Treatment (7 papers) and Radiomics and Machine Learning in Medical Imaging (6 papers). Michael Bockmayr collaborates with scholars based in Germany, United States and South Korea. Michael Bockmayr's co-authors include Frederick Klauschen, Jan Budczies, Carsten Denkert, Albrecht Stenzinger, Manfred Dietel, Jochen K. Lennerz, Wilko Weichert, Ulrich Schüller, Klaus‐Robert Müller and Stephan Wienert and has published in prestigious journals such as Nucleic Acids Research, Journal of Clinical Investigation and SHILAP Revista de lepidopterología.

In The Last Decade

Michael Bockmayr

32 papers receiving 1.4k citations

Peers

Michael Bockmayr
Nicholas P. Tobin Sweden
Gregory Riedlinger United States
Hui Zeng China
Jin Roh South Korea
Lingchuan Guo China
Joseph S. Krueger United States
Nicola Johnson United Kingdom
Xiaobo Zhou United States
Peng Xu China
Jiayi Zhang China
Nicholas P. Tobin Sweden
Michael Bockmayr
Citations per year, relative to Michael Bockmayr Michael Bockmayr (= 1×) peers Nicholas P. Tobin

Countries citing papers authored by Michael Bockmayr

Since Specialization
Citations

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

Fields of papers citing papers by Michael Bockmayr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Bockmayr

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Bockmayr. A scholar is included among the top collaborators of Michael Bockmayr 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 Michael Bockmayr. Michael Bockmayr 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.
Keyl, Julius, Andreas Möck, Liliana H. Mochmann, et al.. (2025). Neural interaction explainable AI predicts drug response across cancers. NAR Cancer. 7(3). zcaf029–zcaf029.
2.
Benesch, Martin, Martin Mynarek, Denise Obrecht, et al.. (2024). Impact of molecular classification on prognosis in children and adolescents with spinal ependymoma: Results from the HIT-MED database. Neuro-Oncology Advances. 6(1). vdae179–vdae179.
3.
Obrecht, Denise, Leonille Schweizer, Annika K. Wefers, et al.. (2024). Molecular characteristics and improved survival prediction in a cohort of 2023 ependymomas. Acta Neuropathologica. 147(1). 24–24. 9 indexed citations
4.
Schweizer, Leonille, et al.. (2024). Nanopore sequencing from formalin-fixed paraffin-embedded specimens for copy-number profiling and methylation-based CNS tumor classification. Acta Neuropathologica. 147(1). 74–74. 6 indexed citations
5.
Bischoff, Philip, Michael Bockmayr, David Horst, et al.. (2023). Single-cell gene regulatory network prediction by explainable AI. Nucleic Acids Research. 51(4). e20–e20. 24 indexed citations
6.
Bockmayr, Michael, et al.. (2023). Rapid Determination of Nutmeg Shell Content in Ground Nutmeg Using FT-NIR Spectroscopy and Machine Learning. Foods. 12(15). 2939–2939. 3 indexed citations
7.
Kresbach, Catena, Mario M. Dorostkar, Patrick N. Harter, et al.. (2023). Molecular refinement of pilocytic astrocytoma in adult patients. Neuropathology and Applied Neurobiology. 50(1).
8.
Bockmayr, Michael, et al.. (2022). Patient-level proteomic network prediction by explainable artificial intelligence. npj Precision Oncology. 6(1). 35–35. 20 indexed citations
9.
Bockmayr, Michael, et al.. (2021). Medulloblastoma tumor classification using deep transfer learning with multi-scale EfficientNets. 10–10. 9 indexed citations
10.
Stenzinger, Albrecht, Maximilian Alber, Michael Allgäuer, et al.. (2021). Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling. Seminars in Cancer Biology. 84. 129–143. 56 indexed citations
11.
Maire, Cécile L., Malte Mohme, Michael Bockmayr, et al.. (2020). Glioma escape signature and clonal development under immune pressure. Journal of Clinical Investigation. 130(10). 5257–5271. 28 indexed citations
12.
Bockmayr, Michael, Frederick Klauschen, Cécile L. Maire, et al.. (2019). Immunologic Profiling of Mutational and Transcriptional Subgroups in Pediatric and Adult High-Grade Gliomas. Cancer Immunology Research. 7(9). 1401–1411. 27 indexed citations
13.
Jurmeister, Philipp, Michael Bockmayr, Philipp Seegerer, et al.. (2019). Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases. Science Translational Medicine. 11(509). 78 indexed citations
14.
Lauffer, Marlen C., Michael Bockmayr, Michael Spohn, et al.. (2019). TCF4 (E2-2) harbors tumor suppressive functions in SHH medulloblastoma. Acta Neuropathologica. 137(4). 657–673. 18 indexed citations
15.
Bockmayr, Michael, et al.. (2019). Food authentication: Multi-elemental analysis of white asparagus for provenance discrimination. Food Chemistry. 286. 475–482. 46 indexed citations
16.
Treue, Denise, Michael Bockmayr, Albrecht Stenzinger, et al.. (2018). Proteogenomic systems analysis identifies targeted therapy resistance mechanisms in EGFR‐mutated lung cancer. International Journal of Cancer. 144(3). 545–557. 7 indexed citations
17.
Niehr, Franziska, Robert Konschak, Denise Treue, et al.. (2017). Multilayered Omics-Based Analysis of a Head and Neck Cancer Model of Cisplatin Resistance Reveals Intratumoral Heterogeneity and Treatment-Induced Clonal Selection. Clinical Cancer Research. 24(1). 158–168. 41 indexed citations
18.
Budczies, Jan, Michael Bockmayr, Frederick Klauschen, et al.. (2017). Mutation patterns in genes encoding interferon signaling and antigen presentation: A pan‐cancer survey with implications for the use of immune checkpoint inhibitors. Genes Chromosomes and Cancer. 56(8). 651–659. 32 indexed citations
19.
Volckmar, Anna‐Lena, Jonas Leichsenring, Christa Flechtenmacher, et al.. (2016). Tubular, lactating, and ductal adenomas are devoid of MED12 Exon2 mutations, and ductal adenomas show recurrent mutations in GNAS and the PI3K–AKT pathway. Genes Chromosomes and Cancer. 56(1). 11–17. 17 indexed citations
20.
Budczies, Jan, Michael Bockmayr, Carsten Denkert, et al.. (2015). Classical pathology and mutational load of breast cancer – integration of two worlds. The Journal of Pathology Clinical Research. 1(4). 225–238. 84 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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