Jan Niehues

2.0k total citations
20 papers, 445 citations indexed

About

Jan Niehues is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence and Oncology. According to data from OpenAlex, Jan Niehues has authored 20 papers receiving a total of 445 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Nuclear and High Energy Physics, 6 papers in Artificial Intelligence and 4 papers in Oncology. Recurrent topics in Jan Niehues's work include Particle physics theoretical and experimental studies (10 papers), High-Energy Particle Collisions Research (10 papers) and Quantum Chromodynamics and Particle Interactions (10 papers). Jan Niehues is often cited by papers focused on Particle physics theoretical and experimental studies (10 papers), High-Energy Particle Collisions Research (10 papers) and Quantum Chromodynamics and Particle Interactions (10 papers). Jan Niehues collaborates with scholars based in United Kingdom, Germany and Switzerland. Jan Niehues's co-authors include T. Gehrmann, Alexander Huss, James Currie, Jakob Nikolas Kather, E. W. N. Glover, A. Vogt, Daniel Truhn, D. Walker, Christiane Kühl and Firas Khader and has published in prestigious journals such as Physical Review Letters, Oncogene and Scientific Reports.

In The Last Decade

Jan Niehues

19 papers receiving 441 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jan Niehues United Kingdom 12 154 116 112 73 43 20 445
Sarah Eskreis‐Winkler United States 12 15 0.1× 447 3.9× 103 0.9× 26 0.4× 13 0.3× 33 617
Alberto Romagnoni France 10 225 1.5× 110 0.9× 151 1.3× 58 0.8× 1 0.0× 18 602
Rhodri Smith United Kingdom 11 12 0.1× 73 0.6× 23 0.2× 71 1.0× 11 0.3× 39 422
T. Whyntie United Kingdom 6 31 0.2× 179 1.5× 111 1.0× 11 0.2× 7 0.2× 10 408
Matthias Jung Germany 12 12 0.1× 134 1.2× 52 0.5× 47 0.6× 4 0.1× 40 460
K. Yamauchi Japan 14 80 0.5× 75 0.6× 32 0.3× 67 0.9× 3 0.1× 46 689
Feng Su China 10 8 0.1× 87 0.8× 70 0.6× 33 0.5× 4 0.1× 28 340
Peter M. Maloca Switzerland 14 24 0.2× 539 4.6× 17 0.2× 16 0.2× 7 0.2× 70 849
Eric S. Diffenderfer United States 20 68 0.4× 366 3.2× 27 0.2× 77 1.1× 8 0.2× 54 1.0k
Matthias Bussas Germany 9 19 0.1× 101 0.9× 17 0.2× 15 0.2× 15 0.3× 21 309

Countries citing papers authored by Jan Niehues

Since Specialization
Citations

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

Fields of papers citing papers by Jan Niehues

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jan Niehues

This figure shows the co-authorship network connecting the top 25 collaborators of Jan Niehues. A scholar is included among the top collaborators of Jan Niehues 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 Jan Niehues. Jan Niehues 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.
2.
Liu, Danni & Jan Niehues. (2025). Middle-Layer Representation Alignment for Cross-Lingual Transfer in Fine-Tuned LLMs. 15979–15996. 1 indexed citations
3.
Niehues, Jan, Gustav Müller‐Franzes, Sophia J. Wagner, et al.. (2024). Using histopathology latent diffusion models as privacy-preserving dataset augmenters improves downstream classification performance. Computers in Biology and Medicine. 175. 108410–108410. 5 indexed citations
4.
Saldanha, Oliver Lester, Chiara Maria Lavinia Loeffler, Jan Niehues, et al.. (2023). Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology. npj Precision Oncology. 7(1). 35–35. 41 indexed citations
5.
Niehues, Jan, Philip Quirke, Nicholas P. West, et al.. (2023). Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study. Cell Reports Medicine. 4(4). 100980–100980. 46 indexed citations
6.
Müller‐Franzes, Gustav, Jan Niehues, Firas Khader, et al.. (2023). A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis. Scientific Reports. 13(1). 12098–12098. 79 indexed citations
7.
Niehues, Jan, Marko van Treeck, Chiara Maria Lavinia Loeffler, et al.. (2023). Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer. npj Breast Cancer. 9(1). 91–91. 3 indexed citations
8.
Heij, Lara R., Xiuxiang Tan, Jakob Nikolas Kather, et al.. (2021). Nerve Fibers in the Tumor Microenvironment Are Co-Localized with Lymphoid Aggregates in Pancreatic Cancer. Journal of Clinical Medicine. 10(3). 490–490. 17 indexed citations
9.
Tan, Xiuxiang, Shivan Sivakumar, Jan Bednarsch, et al.. (2020). Nerve fibers in the tumor microenvironment in neurotropic cancer—pancreatic cancer and cholangiocarcinoma. Oncogene. 40(5). 899–908. 76 indexed citations
10.
Muti, Hannah Sophie, Chiara Maria Lavinia Loeffler, Amelie Echle, et al.. (2020). The Aachen Protocol for Deep Learning Histopathology: A hands-on guide for data preprocessing. Zenodo (CERN European Organization for Nuclear Research). 16 indexed citations
11.
Gehrmann, T., et al.. (2019). Second-order QCD corrections to event shape distributions in deep inelastic scattering. The European Physical Journal C. 79(12). 1022–1022. 7 indexed citations
12.
Gehrmann, T., Alexander Huss, Jan Niehues, A. Vogt, & D. Walker. (2019). Jet production in charged-current deep-inelastic scattering to third order in QCD. Physics Letters B. 792. 182–186. 21 indexed citations
13.
Britzger, D., James Currie, A. Gehrmann–De Ridder, et al.. (2019). Calculations for deep inelastic scattering using fast interpolation grid techniques at NNLO in QCD and the extraction of $\alpha _{\mathrm {s}}$ from HERA data. Repository KITopen (Karlsruhe Institute of Technology). 8 indexed citations
14.
Currie, James, T. Gehrmann, E. W. N. Glover, et al.. (2018). N3LO corrections to jet production in deep inelastic scattering using the Projection-to-Born method. Zurich Open Repository and Archive (University of Zurich). 40 indexed citations
15.
Gehrmann, T., Xuan Chen, Juan Cruz–Martinez, et al.. (2018). Jet cross sections and transverse momentume distributions with NNLOJET. Zurich Open Repository and Archive (University of Zurich). 74–74. 11 indexed citations
16.
Britzger, D., James Currie, T. Gehrmann, et al.. (2018). Dijet production in diffractive deep-inelastic scattering in next-to-next-to-leading order QCD. The European Physical Journal C. 78(7). 538–538. 10 indexed citations
17.
Niehues, Jan & D. Walker. (2018). NNLO QCD corrections to jet production in charged current deep inelastic scattering. Physics Letters B. 788. 243–248. 7 indexed citations
18.
Currie, James, T. Gehrmann, Alexander Huss, & Jan Niehues. (2017). NNLO QCD corrections to jet production in deep inelastic scattering. Journal of High Energy Physics. 2017(7). 23 indexed citations
19.
Gehrmann, T., E. W. N. Glover, Alexander Huss, Jan Niehues, & Hantian Zhang. (2017). NNLO QCD corrections to event orientation in e+e− annihilation. Physics Letters B. 775. 185–189. 12 indexed citations
20.
Currie, James, T. Gehrmann, & Jan Niehues. (2016). Precise QCD Predictions for the Production of Dijet Final States in Deep Inelastic Scattering. Physical Review Letters. 117(4). 42001–42001. 22 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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