Ina Koch

7.9k total citations · 1 hit paper
107 papers, 3.3k citations indexed

About

Ina Koch is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Materials Chemistry. According to data from OpenAlex, Ina Koch has authored 107 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 72 papers in Molecular Biology, 15 papers in Radiology, Nuclear Medicine and Imaging and 15 papers in Materials Chemistry. Recurrent topics in Ina Koch's work include Gene Regulatory Network Analysis (30 papers), Microbial Metabolic Engineering and Bioproduction (26 papers) and Bioinformatics and Genomic Networks (24 papers). Ina Koch is often cited by papers focused on Gene Regulatory Network Analysis (30 papers), Microbial Metabolic Engineering and Bioproduction (26 papers) and Bioinformatics and Genomic Networks (24 papers). Ina Koch collaborates with scholars based in Germany, United States and Luxembourg. Ina Koch's co-authors include Monika Heiner, Jörg Ackermann, Andrea Sackmann, Thomas Lengauer, James R. Halpert, Renzo Wolbold, Stefan Schuster, Michael Haberl, Arne Zibat and P. Neuhaus and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Ina Koch

102 papers receiving 3.2k citations

Hit Papers

The genetic determinants of the CYP3A5 polymorphism 2001 2026 2009 2017 2001 100 200 300 400 500
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.

  1. The genetic determinants of the CYP3A5 polymorphism
    2001 560 citations Ina Koch et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ina Koch Germany 26 2.1k 373 373 325 298 107 3.3k
Qingyu Zhou United States 26 797 0.4× 652 1.7× 256 0.7× 53 0.2× 248 0.8× 91 2.6k
Peter Shaw United States 27 1.3k 0.6× 935 2.5× 582 1.6× 148 0.5× 581 1.9× 117 3.3k
Aris Floratos United States 15 1.1k 0.5× 291 0.8× 527 1.4× 185 0.6× 108 0.4× 26 2.5k
Natali Gulbahce United States 15 2.8k 1.3× 108 0.3× 106 0.3× 778 2.4× 359 1.2× 26 4.2k
Neema Jamshidi United States 32 2.8k 1.3× 273 0.7× 111 0.3× 215 0.7× 376 1.3× 71 4.0k
Seiichi Ishida Japan 24 2.5k 1.2× 1.4k 3.7× 297 0.8× 79 0.2× 580 1.9× 69 3.8k
Habtom W. Ressom United States 36 3.5k 1.7× 445 1.2× 89 0.2× 191 0.6× 668 2.2× 170 5.1k
Christopher W.V. Hogue Canada 25 6.9k 3.3× 422 1.1× 123 0.3× 975 3.0× 770 2.6× 62 8.9k
Peter Marbach Canada 34 791 0.4× 873 2.3× 33 0.1× 99 0.3× 78 0.3× 112 4.9k

Countries citing papers authored by Ina Koch

Since Specialization
Citations

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

Fields of papers citing papers by Ina Koch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ina Koch

This figure shows the co-authorship network connecting the top 25 collaborators of Ina Koch. A scholar is included among the top collaborators of Ina Koch 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 Ina Koch. Ina Koch 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.
Matthias, Christoph, et al.. (2026). Sequential anticoagulation with LMWH and DOACs in cirrhotic portal vein thrombosis. Clinics and Research in Hepatology and Gastroenterology. 50(4). 102792–102792.
2.
Bernatz, Simon, Leon D. Gruenewald, Ina Koch, et al.. (2024). Assessment of thoracic disc degeneration using dual-energy CT-based collagen maps. European Radiology Experimental. 8(1). 95–95.
3.
4.
Ackermann, Jörg, et al.. (2024). Computational systems biology of cellular processes in the human lymph node. Current Opinion in Systems Biology. 38. 100518–100518. 1 indexed citations
5.
D’Angelo, Tommaso, İbrahim Yel, Ina Koch, et al.. (2024). Accuracy and time efficiency of a novel deep learning algorithm for Intracranial Hemorrhage detection in CT Scans. La radiologia medica. 129(10). 1499–1506. 3 indexed citations
6.
Ackermann, Jörg, et al.. (2023). Holistic View on the Structure of Immune Response: Petri Net Model. Biomedicines. 11(2). 452–452. 4 indexed citations
7.
Koch, Ina & Bianca Büttner. (2023). Computational modeling of signal transduction networks without kinetic parameters: Petri net approaches. American Journal of Physiology-Cell Physiology. 324(5). C1126–C1140. 5 indexed citations
8.
9.
Ackermann, Jörg, et al.. (2023). Variation of butyrate production in the gut microbiome in type 2 diabetes patients. International Microbiology. 26(3). 601–610. 22 indexed citations
10.
Ackermann, Jörg, et al.. (2023). Investigation of metabolic pathways from gut microbiome analyses regarding type 2 diabetes mellitus using artificial neural networks. SHILAP Revista de lepidopterología. 3(1). 5 indexed citations
11.
Ackermann, Jörg, et al.. (2022). Mathematical modeling of the molecular switch of TNFR1-mediated signaling pathways applying Petri net formalism and in silico knockout analysis. PLoS Computational Biology. 18(8). e1010383–e1010383. 5 indexed citations
12.
Ackermann, Jörg, et al.. (2021). 3D connectomes of reactive and neoplastic CD30 positive lymphoid cells and surrounding cell types. Acta Histochemica. 123(5). 151750–151750. 4 indexed citations
13.
Ackermann, Jörg, Tim Schäfer, Claudia Döring, et al.. (2020). Bioinformatics analysis of whole slide images reveals significant neighborhood preferences of tumor cells in Hodgkin lymphoma. PLoS Computational Biology. 16(1). e1007516–e1007516. 12 indexed citations
14.
Ackermann, Jörg, et al.. (2018). isiKnock: in silico knockouts in signaling pathways. Bioinformatics. 35(5). 892–894. 6 indexed citations
15.
16.
Heide, Heinrich, Lea Bleier, Mirco Steger, et al.. (2012). Complexome Profiling Identifies TMEM126B as a Component of the Mitochondrial Complex I Assembly Complex. Cell Metabolism. 16(4). 538–549. 224 indexed citations
17.
Rother, Kristian, Heiko Müller, Thomas Steinke, et al.. (2005). Columba: an integrated database of proteins, structures, and annotations. BMC Bioinformatics. 6(1). 81–81. 31 indexed citations
18.
Koch, Ina. (2001). Enumerating all connected maximal common subgraphs in two graphs. Theoretical Computer Science. 250(1-2). 1–30. 149 indexed citations
19.
Schuster, Stefan, Thomas Pfeiffer, F. Moldenhauer, Ina Koch, & Thomas Dandekar. (2000). Structural analysis of metabolic networks: elementary flux modes, analogy to petri nets, and application to Mycoplasma pneumoniae. 115–120. 10 indexed citations
20.
Koch, Ina, Stefan Schuster, & Monika Heiner. (1999). Simulation and analysis of metabolic networks by time-dependent Petri nets.. 208–209. 4 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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