Jörg Ackermann

1.3k total citations
50 papers, 812 citations indexed

About

Jörg Ackermann is a scholar working on Molecular Biology, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Jörg Ackermann has authored 50 papers receiving a total of 812 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Molecular Biology, 9 papers in Artificial Intelligence and 9 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Jörg Ackermann's work include Gene Regulatory Network Analysis (14 papers), Bioinformatics and Genomic Networks (10 papers) and Microbial Metabolic Engineering and Bioproduction (10 papers). Jörg Ackermann is often cited by papers focused on Gene Regulatory Network Analysis (14 papers), Bioinformatics and Genomic Networks (10 papers) and Microbial Metabolic Engineering and Bioproduction (10 papers). Jörg Ackermann collaborates with scholars based in Germany, Austria and Switzerland. Jörg Ackermann's co-authors include Ina Koch, Robert Penchovsky, Ilka Wittig, Ulrich Brandt, Heinrich Heide, Stefan Dröse, Lea Bleier, Andreas S. Reichert, Mirco Steger and Martin Zörnig and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Cell Metabolism.

In The Last Decade

Jörg Ackermann

49 papers receiving 801 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jörg Ackermann Germany 15 588 63 62 55 55 50 812
Anastasios Matzavinos United States 14 408 0.7× 22 0.3× 152 2.5× 15 0.3× 43 0.8× 30 857
Jean‐Marc Steyaert France 16 714 1.2× 9 0.1× 77 1.2× 49 0.9× 24 0.4× 42 1.1k
Armin Graber Austria 17 507 0.9× 20 0.3× 93 1.5× 25 0.5× 14 0.3× 44 854
Mikhail A. Pyatnitskiy Russia 16 537 0.9× 11 0.2× 69 1.1× 23 0.4× 49 0.9× 48 875
Trung Nghia Vu Sweden 16 579 1.0× 11 0.2× 44 0.7× 43 0.8× 16 0.3× 49 899
Srikant Verma India 5 595 1.0× 9 0.1× 49 0.8× 42 0.8× 38 0.7× 5 891
Richard K. Lee United States 18 382 0.6× 18 0.3× 42 0.7× 16 0.3× 208 3.8× 75 1.0k
Marek Ostaszewski Luxembourg 15 400 0.7× 8 0.1× 59 1.0× 42 0.8× 22 0.4× 48 661
Walid M. Abdelmoula United States 17 464 0.8× 14 0.2× 57 0.9× 30 0.5× 91 1.7× 23 806
Lai Hong Wong Canada 9 563 1.0× 10 0.2× 29 0.5× 25 0.5× 27 0.5× 17 898

Countries citing papers authored by Jörg Ackermann

Since Specialization
Citations

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

Fields of papers citing papers by Jörg Ackermann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jörg Ackermann

This figure shows the co-authorship network connecting the top 25 collaborators of Jörg Ackermann. A scholar is included among the top collaborators of Jörg Ackermann 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 Jörg Ackermann. Jörg Ackermann 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
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2.
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
3.
Mahmoudi, Scherwin, Simon Bernatz, Jörg Ackermann, et al.. (2023). Computed Tomography Radiomics to Differentiate Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma. Clinical Oncology. 35(5). e312–e318. 8 indexed citations
4.
Ackermann, Jörg, et al.. (2023). Holistic View on the Structure of Immune Response: Petri Net Model. Biomedicines. 11(2). 452–452. 4 indexed citations
5.
6.
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
7.
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
8.
Bernatz, Simon, Jörg Ackermann, Iris Burck, et al.. (2023). Radiomics for therapy-specific head and neck squamous cell carcinoma survival prognostication (part I). BMC Medical Imaging. 23(1). 71–71. 6 indexed citations
9.
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
10.
Bernatz, Simon, Scherwin Mahmoudi, Simon S. Martin, et al.. (2021). Differences in mastoid and middle-ear cavity opacification in CT between intensive care patients and patients with acute mastoiditis requiring surgical treatment. European Journal of Radiology Open. 8. 100365–100365. 1 indexed citations
11.
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
12.
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
13.
Ackermann, Jörg, et al.. (2018). isiKnock: in silico knockouts in signaling pathways. Bioinformatics. 35(5). 892–894. 6 indexed citations
14.
Wegner, Martin, Jörg Ackermann, Ina Koch, et al.. (2017). APP Deletion Accounts for Age-Dependent Changes in the Bioenergetic Metabolism and in Hyperphosphorylated CaMKII at Stimulated Hippocampal Presynaptic Active Zones. Frontiers in Synaptic Neuroscience. 9. 1–1. 9 indexed citations
15.
Ackermann, Jörg, et al.. (2016). In Silico Knockout Studies of Xenophagic Capturing of Salmonella. PLoS Computational Biology. 12(12). e1005200–e1005200. 17 indexed citations
16.
Schäfer, Tim, Alexander Schmitz, Jörg Ackermann, et al.. (2013). Image database analysis of Hodgkin lymphoma. Computational Biology and Chemistry. 46. 1–7. 13 indexed citations
17.
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
18.
Speer, Astrid, et al.. (2008). Petri net modelling of gene regulation of the Duchenne muscular dystrophy. Biosystems. 92(2). 189–205. 52 indexed citations
19.
Ackermann, Jörg, et al.. (2005). Optimization and design of oligonucleotide setup for strand displacement amplification. Journal of Biochemical and Biophysical Methods. 63(3). 170–186. 16 indexed citations
20.
Ackermann, Jörg, et al.. (1999). Complex patterns predicted in an in vitro experimental model system for the evolution of molecular cooperation. Biophysical Chemistry. 79(3). 163–186. 5 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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