Jonathan Hoser

632 total citations
8 papers, 480 citations indexed

About

Jonathan Hoser is a scholar working on Molecular Biology, Oncology and Immunology. According to data from OpenAlex, Jonathan Hoser has authored 8 papers receiving a total of 480 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 2 papers in Oncology and 2 papers in Immunology. Recurrent topics in Jonathan Hoser's work include Genomics and Phylogenetic Studies (3 papers), Machine Learning in Bioinformatics (2 papers) and Viral-associated cancers and disorders (2 papers). Jonathan Hoser is often cited by papers focused on Genomics and Phylogenetic Studies (3 papers), Machine Learning in Bioinformatics (2 papers) and Viral-associated cancers and disorders (2 papers). Jonathan Hoser collaborates with scholars based in Germany, United States and Austria. Jonathan Hoser's co-authors include Bill Sugden, Mitchell Hayes, Dominik Lutter, Andreas Moosmann, Wolfgang Hammerschmidt, Takanobu Tagawa, Mickaël Bouvet, Manuel Albanese, Maximilian Hastreiter and Hans‐Werner Mewes and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Genetics.

In The Last Decade

Jonathan Hoser

8 papers receiving 472 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jonathan Hoser Germany	6	284	174	95	93	91	8	480
Merete Rasmussen Denmark	7	339 1.2×	49 0.3×	68 0.7×	43 0.5×	35 0.4×	8	442
Edward R. Hofmann United States	8	276 1.0×	105 0.6×	62 0.7×	11 0.1×	117 1.3×	11	402
J Dittmer United States	11	253 0.9×	105 0.6×	58 0.6×	11 0.1×	202 2.2×	14	575
Frank Korioth Germany	7	290 1.0×	63 0.4×	34 0.4×	19 0.2×	90 1.0×	7	490
Suat Peng Neo Singapore	10	321 1.1×	129 0.7×	81 0.9×	12 0.1×	21 0.2×	18	446
Marion Piganeau France	5	474 1.7×	69 0.4×	61 0.6×	48 0.5×	14 0.2×	6	506
Mariana Justino de Almeida United States	7	396 1.4×	60 0.3×	80 0.8×	17 0.2×	94 1.0×	7	638
Johannes Popow Austria	11	1.0k 3.7×	70 0.4×	85 0.9×	20 0.2×	58 0.6×	14	1.2k
Olga P. Ryabinina United States	9	242 0.9×	47 0.3×	35 0.4×	11 0.1×	103 1.1×	12	396
Esther Ortega Spain	9	608 2.1×	85 0.5×	58 0.6×	9 0.1×	34 0.4×	20	792

Countries citing papers authored by Jonathan Hoser

Since Specialization

Citations

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

Fields of papers citing papers by Jonathan Hoser

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jonathan Hoser

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

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Hastreiter, Maximilian, Tim Jeske, Jonathan Hoser, et al.. (2017). KNIME4NGS: a comprehensive toolbox for next generation sequencing analysis. Bioinformatics. 33(10). 1565–1567. 4 indexed citations

2.

Albanese, Manuel, Takanobu Tagawa, Mickaël Bouvet, et al.. (2016). Epstein–Barr virus microRNAs reduce immune surveillance by virus-specific CD8 ⁺ T cells. Proceedings of the National Academy of Sciences. 113(42). E6467–E6475. 127 indexed citations

3.

Tagawa, Takanobu, Manuel Albanese, Mickaël Bouvet, et al.. (2016). Epstein-Barr viral miRNAs inhibit antiviral CD4+ T cell responses targeting IL-12 and peptide processing. The Journal of Experimental Medicine. 213(10). 2065–2080. 105 indexed citations

4.

Weinmaier, Thomas, Jonathan Hoser, Sebastian Eck, et al.. (2015). Genomic factors related to tissue tropism in Chlamydia pneumoniae infection. BMC Genomics. 16(1). 268–268. 11 indexed citations

5.

Li, Dan, Michael Rothballer, Marion Engel, et al.. (2011). Phenotypic variation in Acidovorax radicisN35 influences plant growth promotion. FEMS Microbiology Ecology. 79(3). 751–762. 18 indexed citations

6.

Haack, Tobias B., Katharina Danhauser, Birgit Haberberger, et al.. (2010). Exome sequencing identifies ACAD9 mutations as a cause of complex I deficiency. Nature Genetics. 42(12). 1131–1134. 179 indexed citations

7.

Rattei, Thomas, Patrick Tischler, Stefan Götz, et al.. (2010). SIMAP - a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters. 38(1). 1 indexed citations

8.

Rattei, Thomas, Patrick Tischler, Stefan Götz, et al.. (2009). SIMAP—a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters. Nucleic Acids Research. 38(suppl_1). D223–D226. 35 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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