Frederick Klauschen

25.5k total citations · 2 hit papers
201 papers, 10.5k citations indexed

About

Frederick Klauschen is a scholar working on Cancer Research, Oncology and Molecular Biology. According to data from OpenAlex, Frederick Klauschen has authored 201 papers receiving a total of 10.5k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Cancer Research, 69 papers in Oncology and 59 papers in Molecular Biology. Recurrent topics in Frederick Klauschen's work include Cancer Genomics and Diagnostics (64 papers), Lung Cancer Treatments and Mutations (25 papers) and Cancer Immunotherapy and Biomarkers (21 papers). Frederick Klauschen is often cited by papers focused on Cancer Genomics and Diagnostics (64 papers), Lung Cancer Treatments and Mutations (25 papers) and Cancer Immunotherapy and Biomarkers (21 papers). Frederick Klauschen collaborates with scholars based in Germany, United States and United Kingdom. Frederick Klauschen's co-authors include Klaus‐Robert Müller, Grégoire Montavon, Alexander Binder, Wojciech Samek, Sebastian Bach, Ronald N. Germain, Carsten Denkert, Jan Budczies, Albrecht Stenzinger and Balázs Győrffy and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Frederick Klauschen

190 papers receiving 10.4k 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.

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

2015 2.5k citations Grégoire Montavon, Frederick Klauschen et al. profile →
Cutoff Finder: A Comprehensive and Straightforward Web Application Enabling Rapid Biomarker Cutoff Optimization

2012 954 citations Jan Budczies, Frederick Klauschen et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Frederick Klauschen Germany	46	2.8k	2.6k	2.5k	2.1k	1.5k	201	10.5k
Hemant Ishwaran United States	47	2.5k 0.9×	2.2k 0.8×	1.4k 0.5×	2.5k 1.2×	1.2k 0.8×	107	11.5k
Peter W. Hamilton United Kingdom	31	2.2k 0.8×	1.8k 0.7×	942 0.4×	1.3k 0.6×	845 0.6×	115	7.5k
Stephen T.C. Wong United States	59	6.8k 2.5×	2.9k 1.1×	1.1k 0.4×	1.2k 0.6×	1.8k 1.2×	493	16.9k
Rob Tibshirani United States	19	4.7k 1.7×	1.7k 0.7×	815 0.3×	1.2k 0.6×	2.4k 1.6×	28	14.2k
Christine Decaestecker Belgium	52	3.5k 1.3×	1.6k 0.6×	2.2k 0.9×	567 0.3×	937 0.6×	287	8.7k
Stefano Monti United States	45	5.8k 2.1×	2.8k 1.1×	1.9k 0.8×	797 0.4×	1.8k 1.2×	210	12.3k
Yvan Saeys Belgium	49	8.5k 3.1×	1.2k 0.4×	4.2k 1.7×	2.1k 1.0×	1.1k 0.8×	196	16.4k
Joseph G. Ibrahim United States	60	3.1k 1.1×	2.8k 1.1×	1.7k 0.7×	2.8k 1.4×	784 0.5×	397	16.6k
Michael D. Feldman United States	63	4.3k 1.6×	5.1k 1.9×	2.7k 1.1×	3.6k 1.7×	1.8k 1.2×	276	15.3k
Susan M. Swetter United States	42	2.3k 0.8×	6.7k 2.6×	1.9k 0.8×	3.7k 1.8×	667 0.4×	141	15.1k

Countries citing papers authored by Frederick Klauschen

Since Specialization

Citations

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

Fields of papers citing papers by Frederick Klauschen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Frederick Klauschen

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

All Works

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20 of 20 papers shown

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.

Michl, Marlies, Frederick Klauschen, Thomas Kirchner, et al.. (2024). Identification of a gene expression signature associated with brain metastasis in colorectal cancer. Clinical & Translational Oncology. 26(8). 1886–1895. 3 indexed citations

Liokatis, Paris, Wenko Smolka, Sven Otto, et al.. (2024). Prognostic role of lymph node micrometastasis in oral and oropharyngeal cancer: A systematic review. Oral Oncology. 154. 106808–106808. 3 indexed citations

Lange, Philipp, Dorit Di Gioia, M. Schmidt, et al.. (2023). Implementing precision oncology for sarcoma patients: the CCCLMUmolecular tumor board experience. Journal of Cancer Research and Clinical Oncology. 149(15). 13973–13983. 3 indexed citations

Steffen, Claudius, Simon Schallenberg, Mihnea P. Dragomir, et al.. (2023). Spatial heterogeneity of tumor cells and the tissue microenvironment in oral squamous cell carcinoma. Oral Surgery Oral Medicine Oral Pathology and Oral Radiology. 137(4). 379–390. 2 indexed citations

Schweizer, Leonille, Philipp Seegerer, Anja Osterloh, et al.. (2022). Analysing cerebrospinal fluid with explainable deep learning: From diagnostics to insights. Neuropathology and Applied Neurobiology. 49(1). e12866–e12866. 4 indexed citations

Bischoff, Philip, Alexandra Trinks, Benedikt Obermayer, et al.. (2022). The single‐cell transcriptional landscape of lung carcinoid tumors. International Journal of Cancer. 150(12). 2058–2071. 18 indexed citations

Tschurtschenthaler, Markus, Matjaž Rokavec, Miguel G. Silva, et al.. (2022). SMAD4 Loss Induces c-MYC–Mediated NLE1 Upregulation to Support Protein Biosynthesis, Colorectal Cancer Growth, and Metastasis. Cancer Research. 82(24). 4604–4623. 19 indexed citations

Heß, Julia, Christopher Kurz, Marco Riboldi, et al.. (2022). DeepClassPathway: Molecular pathway aware classification using explainable deep learning. European Journal of Cancer. 176. 41–49. 5 indexed citations

10.

Obermayer, Benedikt, Konrad Klinghammer, Sebastian Ochsenreither, et al.. (2020). Distinct immune evasion in APOBEC ‐enriched, HPV ‐negative HNSCC. International Journal of Cancer. 147(8). 2293–2302. 12 indexed citations

11.

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

12.

Cabeza-Cabrerizo, Mar, Janneke van Blijswijk, Stephan Wienert, et al.. (2019). Tissue clonality of dendritic cell subsets and emergency DCpoiesis revealed by multicolor fate mapping of DC progenitors. Science Immunology. 4(33). 78 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.

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

15.

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

16.

Leichsenring, Jonas, Anna‐Lena Volckmar, Martina Kirchner, et al.. (2017). Targeted deep sequencing of effusion cytology samples is feasible, informs spatiotemporal tumor evolution, and has clinical and diagnostic utility. Genes Chromosomes and Cancer. 57(2). 70–79. 13 indexed citations

17.

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

18.

Klauschen, Frederick, Stephan Wienert, Wolfgang Schmitt, et al.. (2014). Standardized Ki67 Diagnostics Using Automated Scoring—Clinical Validation in the GeparTrio Breast Cancer Study. Clinical Cancer Research. 21(16). 3651–3657. 78 indexed citations

19.

Budczies, Jan, Moritz von Winterfeld, Frederick Klauschen, et al.. (2014). Comprehensive analysis of clinico-pathological data reveals heterogeneous relations between atherosclerosis and cancer. Journal of Clinical Pathology. 67(6). 482–490. 13 indexed citations

20.

Mandl, Judith N., Frederick Klauschen, Nienke Vrisekoop, et al.. (2012). Quantification of lymph node transit times reveals differences in antigen surveillance strategies of naïve CD4 ⁺ and CD8 ⁺ T cells. Proceedings of the National Academy of Sciences. 109(44). 18036–18041. 121 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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