Daniel Heim

1.1k total citations
17 papers, 578 citations indexed

About

Daniel Heim is a scholar working on Cancer Research, Molecular Biology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Daniel Heim has authored 17 papers receiving a total of 578 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Cancer Research, 8 papers in Molecular Biology and 6 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Daniel Heim's work include Cancer Genomics and Diagnostics (11 papers), Lung Cancer Treatments and Mutations (6 papers) and AI in cancer detection (4 papers). Daniel Heim is often cited by papers focused on Cancer Genomics and Diagnostics (11 papers), Lung Cancer Treatments and Mutations (6 papers) and AI in cancer detection (4 papers). Daniel Heim collaborates with scholars based in Germany, United Kingdom and United States. Daniel Heim's co-authors include Frederick Klauschen, Stephan Wienert, Albrecht Stenzinger, Peter Hufnagl, Carsten Denkert, Manfred Dietel, Michael Beil, Kai Saeger, Klaus‐Robert Müller and Michael Bockmayr and has published in prestigious journals such as Scientific Reports, International Journal of Cancer and The Journal of Pathology.

In The Last Decade

Daniel Heim

17 papers receiving 560 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Heim Germany 10 253 159 142 125 115 17 578
Tokiya Abe Japan 15 166 0.7× 150 0.9× 89 0.6× 94 0.8× 96 0.8× 49 645
Quirine F. Manson Netherlands 8 265 1.0× 116 0.7× 195 1.4× 89 0.7× 66 0.6× 9 490
Marcial García‐Rojo Spain 15 349 1.4× 155 1.0× 181 1.3× 109 0.9× 153 1.3× 57 682
Henrik Failmezger Germany 9 211 0.8× 68 0.4× 144 1.0× 337 2.7× 116 1.0× 15 671
Bassem Ben Cheikh France 6 443 1.8× 344 2.2× 299 2.1× 136 1.1× 80 0.7× 18 778
Lauri Goodell United States 16 183 0.7× 137 0.9× 81 0.6× 220 1.8× 71 0.6× 26 620
Marcory van Dijk Netherlands 5 194 0.8× 86 0.5× 171 1.2× 110 0.9× 42 0.4× 5 427
Robert Kornegoor Netherlands 12 285 1.1× 175 1.1× 162 1.1× 111 0.9× 79 0.7× 18 678
Nicole B. Johnson United States 9 112 0.4× 50 0.3× 108 0.8× 140 1.1× 133 1.2× 27 580
Eirini Arvaniti Switzerland 6 246 1.0× 81 0.5× 163 1.1× 148 1.2× 62 0.5× 9 558

Countries citing papers authored by Daniel Heim

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Heim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Heim

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

All Works

17 of 17 papers shown
1.
Bockmayr, Michael, et al.. (2022). Patient-level proteomic network prediction by explainable artificial intelligence. npj Precision Oncology. 6(1). 35–35. 20 indexed citations
2.
Jurmeister, Philipp, Inga Hoffmann, Claudia Vollbrecht, et al.. (2021). Mucosal melanomas of different anatomic sites share a common global DNA methylation profile with cutaneous melanoma but show location‐dependent patterns of genetic and epigenetic alterations. The Journal of Pathology. 256(1). 61–70. 20 indexed citations
3.
Binder, Alexander, Michael Bockmayr, Stephan Wienert, et al.. (2021). Morphological and molecular breast cancer profiling through explainable machine learning. Nature Machine Intelligence. 3(4). 355–366. 103 indexed citations
4.
Erdmann, Gerrit, Denise Treue, Philipp Jurmeister, et al.. (2020). Multiclass cancer classification in fresh frozen and formalin-fixed paraffin-embedded tissue by DigiWest multiplex protein analysis. Laboratory Investigation. 100(10). 1288–1299. 3 indexed citations
5.
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
6.
7.
Keilholz, Ulrich, et al.. (2019). Somatic genome alterations in relation to age in lung adenocarcinoma. International Journal of Cancer. 145(8). 2091–2099. 3 indexed citations
8.
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
9.
Heim, Daniel, Grégoire Montavon, Peter Hufnagl, Klaus‐Robert Müller, & Frederick Klauschen. (2018). Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers. Genome Medicine. 10(1). 83–83. 6 indexed citations
10.
Keilholz, Ulrich, et al.. (2018). Somatic genome alterations in relation to age in lung squamous cell carcinoma. Oncotarget. 9(63). 32161–32172. 2 indexed citations
11.
Sharma, Harshita, Norman Zerbe, Daniel Heim, et al.. (2016). Cell nuclei attributed relational graphs for efficient representation and classification of gastric cancer in digital histopathology. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9791. 97910X–97910X. 10 indexed citations
12.
Pfarr, Nicole, Roland Penzel, Frederick Klauschen, et al.. (2016). Copy number changes of clinically actionable genes in melanoma, non‐small cell lung cancer and colorectal cancer—A survey across 822 routine diagnostic cases. Genes Chromosomes and Cancer. 55(11). 821–833. 34 indexed citations
13.
Sharma, Harshita, Norman Zerbe, Daniel Heim, et al.. (2015). A Multi-resolution Approach for Combining Visual Information using Nuclei Segmentation and Classification in Histopathological Images. 37–46. 46 indexed citations
14.
Klauschen, Frederick, Daniel Heim, & Albrecht Stenzinger. (2015). Histological tumor typing in the age of molecular profiling. Pathology - Research and Practice. 211(12). 897–900. 9 indexed citations
15.
Heim, Daniel, Jan Budczies, Albrecht Stenzinger, et al.. (2014). Cancer beyond organ and tissue specificity: Next‐generation‐sequencing gene mutation data reveal complex genetic similarities across major cancers. International Journal of Cancer. 135(10). 2362–2369. 30 indexed citations
16.
Wienert, Stephan, Daniel Heim, Manato Kotani, et al.. (2013). CognitionMaster: an object-based image analysis framework. Diagnostic Pathology. 8(1). 34–34. 29 indexed citations
17.
Wienert, Stephan, Daniel Heim, Kai Saeger, et al.. (2012). Detection and Segmentation of Cell Nuclei in Virtual Microscopy Images: A Minimum-Model Approach. Scientific Reports. 2(1). 503–503. 170 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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