Christian Ebeling

2.4k total citations
24 papers, 1.2k citations indexed

About

Christian Ebeling is a scholar working on Molecular Biology, Ecology and Computational Theory and Mathematics. According to data from OpenAlex, Christian Ebeling has authored 24 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 6 papers in Ecology and 3 papers in Computational Theory and Mathematics. Recurrent topics in Christian Ebeling's work include Bioinformatics and Genomic Networks (10 papers), Genomics and Phylogenetic Studies (7 papers) and Biomedical Text Mining and Ontologies (6 papers). Christian Ebeling is often cited by papers focused on Bioinformatics and Genomic Networks (10 papers), Genomics and Phylogenetic Studies (7 papers) and Biomedical Text Mining and Ontologies (6 papers). Christian Ebeling collaborates with scholars based in Germany, United Kingdom and Luxembourg. Christian Ebeling's co-authors include Dietmar Schomburg, A. Chang, L.C. Reimer, Jörg Overmann, A. Podstawka, Julia Koblitz, F. Ehrentreich, Maurice Scheer, Oliver Hofmann and Ida Schomburg and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and The Science of The Total Environment.

In The Last Decade

Christian Ebeling

24 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Christian Ebeling Germany 16 800 137 99 86 80 24 1.2k
Anne Siegel France 21 791 1.0× 141 1.0× 271 2.7× 110 1.3× 125 1.6× 93 1.5k
Mark D’Souza United States 15 1.7k 2.1× 246 1.8× 64 0.6× 282 3.3× 42 0.5× 29 2.1k
Nikolai Daraselia United States 16 1.1k 1.3× 37 0.3× 144 1.5× 136 1.6× 162 2.0× 20 1.5k
Zhi‐Liang Ji China 18 911 1.1× 88 0.6× 181 1.8× 80 0.9× 49 0.6× 56 1.4k
Ken‐ichi Iwata Japan 18 315 0.4× 238 1.7× 59 0.6× 87 1.0× 116 1.4× 86 1.1k
Kunxian Shu China 12 984 1.2× 42 0.3× 35 0.4× 121 1.4× 20 0.3× 53 1.7k
Pierre Vincens France 15 1.9k 2.4× 93 0.7× 53 0.5× 171 2.0× 28 0.3× 30 2.4k
Felicity Allen United Kingdom 14 1.3k 1.6× 55 0.4× 100 1.0× 131 1.5× 86 1.1× 18 1.8k

Countries citing papers authored by Christian Ebeling

Since Specialization
Citations

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

Fields of papers citing papers by Christian Ebeling

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Christian Ebeling

This figure shows the co-authorship network connecting the top 25 collaborators of Christian Ebeling. A scholar is included among the top collaborators of Christian Ebeling 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 Christian Ebeling. Christian Ebeling 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.
Rolland, Clara, Johannes Wittmann, L.C. Reimer, et al.. (2024). PhageDive: the comprehensive strain database of prokaryotic viral diversity. Nucleic Acids Research. 53(D1). D819–D825. 4 indexed citations
2.
Schober, Isabel, Julia Koblitz, Christian Ebeling, et al.. (2024). BacDive in 2025: the core database for prokaryotic strain data. Nucleic Acids Research. 53(D1). D748–D756. 25 indexed citations
3.
Raschka, Tamara, et al.. (2023). AI reveals insights into link between CD33 and cognitive impairment in Alzheimer’s Disease. PLoS Computational Biology. 19(2). e1009894–e1009894. 3 indexed citations
4.
Schultz, Bruce, Andrea Zaliani, Stephan Gebel, et al.. (2022). A hybrid approach unveils drug repurposing candidates targeting an Alzheimer pathophysiology mechanism. Patterns. 3(3). 100433–100433. 10 indexed citations
5.
Reimer, L.C., et al.. (2021). BacDive in 2022: the knowledge base for standardized bacterial and archaeal data. Nucleic Acids Research. 50(D1). D741–D746. 136 indexed citations
6.
Nozaki, Ichiro, Tamara Raschka, Christian Ebeling, et al.. (2021). Deletion of Alzheimer's disease‐associated CD33 results in an inflammatory human microglia phenotype. Glia. 69(6). 1393–1412. 71 indexed citations
7.
Domingo‐Fernándéz, Daniel, Shounak Baksi, Bruce Schultz, et al.. (2020). COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology. Bioinformatics. 37(9). 1332–1334. 72 indexed citations
8.
Schatlo, Bawarjan, Oliver Gautschi, Christoph M. Friedrich, et al.. (2019). Association of single and multiple aneurysms with tobacco abuse: an @neurIST risk analysis. Neurosurgical FOCUS. 47(1). E9–E9. 4 indexed citations
9.
Hupfer, Michael, Christiane Herzog, Christian Ebeling, et al.. (2019). Chironomid larvae enhance phosphorus burial in lake sediments: Insights from long-term and short-term experiments. The Science of The Total Environment. 663. 254–264. 42 indexed citations
10.
Domingo‐Fernándéz, Daniel, Alpha Tom Kodamullil, Tamara Raschka, et al.. (2017). Multimodal mechanistic signatures for neurodegenerative diseases (NeuroMMSig): a web server for mechanism enrichment. Bioinformatics. 33(22). 3679–3681. 29 indexed citations
11.
Söhngen, Carola, A. Podstawka, Boyke Bunk, et al.. (2015). BacDive – The Bacterial Diversity Metadatabase in 2016. Nucleic Acids Research. 44(D1). D581–D585. 37 indexed citations
12.
Adhikari, Subash, et al.. (2015). NeuroTransDB: highly curated and structured transcriptomic metadata for neurodegenerative diseases. Database. 2015. bav099–bav099. 14 indexed citations
13.
Hofmann‐Apitius, Martin, G. C. Ball, Stephan Gebel, et al.. (2015). Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders. International Journal of Molecular Sciences. 16(12). 29179–29206. 38 indexed citations
14.
Friedrich, Christoph M., Christian Ebeling, & David Manset. (2010). Cross-Project Uptake of Biomedical Text Mining Results for Candidate Gene Searches.. ERCIM news/ERCIM news online edition. 2010. 45–46. 1 indexed citations
15.
Chang, A., et al.. (2010). The BRENDA Tissue Ontology (BTO): the first all-integrating ontology of all organisms for enzyme sources. Nucleic Acids Research. 39(Database). D507–D513. 131 indexed citations
16.
Ebeling, Christian, et al.. (2007). BRENDA, AMENDA and FRENDA: the enzyme information system in 2007. Nucleic Acids Research. 35(Database). D511–D514. 119 indexed citations
17.
Choi, Claudia, Richard Münch, Stefan Leupold, et al.. (2007). SYSTOMONAS -- an integrated database for systems biology analysis of Pseudomonas. Nucleic Acids Research. 35(Database). D533–D537. 41 indexed citations
18.
Choi, Claudia, Richard Münch, Boyke Bunk, et al.. (2007). Combination of a data warehouse concept with web services for the establishment of the Pseudomonas systems biology database SYSTOMONAS. Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics. 4(1). 12–21. 4 indexed citations
19.
Schomburg, Ida, Antje Chang, Oliver Hofmann, et al.. (2002). BRENDA: a resource for enzyme data and metabolic information. Trends in Biochemical Sciences. 27(1). 54–56. 144 indexed citations
20.

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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