Niels Jahn

720 total citations
10 papers, 317 citations indexed

About

Niels Jahn is a scholar working on Molecular Biology, Computer Networks and Communications and Dermatology. According to data from OpenAlex, Niels Jahn has authored 10 papers receiving a total of 317 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 1 paper in Computer Networks and Communications and 1 paper in Dermatology. Recurrent topics in Niels Jahn's work include RNA and protein synthesis mechanisms (6 papers), RNA Research and Splicing (6 papers) and RNA modifications and cancer (2 papers). Niels Jahn is often cited by papers focused on RNA and protein synthesis mechanisms (6 papers), RNA Research and Splicing (6 papers) and RNA modifications and cancer (2 papers). Niels Jahn collaborates with scholars based in Germany, Austria and France. Niels Jahn's co-authors include Matthias Platzer, Karol Szafranski, Klaus Huse, Rolf Backofen, Michael Hiller, Stefan Schreiber, Jochen Hampe, Rileen Sinha, Martin Bens and Swetlana Nikolajewa and has published in prestigious journals such as Nucleic Acids Research, Nature Genetics and Bioinformatics.

In The Last Decade

Niels Jahn

10 papers receiving 316 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Niels Jahn Germany	8	256	24	19	18	15	10	317
Silvia Jimeno-González Spain	12	476 1.9×	26 1.1×	35 1.8×	19 1.1×	23 1.5×	18	516
Gerrit A.J. Hakkaart Netherlands	7	282 1.1×	20 0.8×	15 0.8×	14 0.8×	20 1.3×	9	427
Gritta Tettweiler Canada	7	222 0.9×	17 0.7×	16 0.8×	47 2.6×	14 0.9×	8	304
Michael Schoof United States	5	195 0.8×	19 0.8×	18 0.9×	13 0.7×	8 0.5×	6	227
Mengnan Cheng China	6	145 0.6×	24 1.0×	17 0.9×	19 1.1×	24 1.6×	10	186
Matthew R. Marunde United States	8	209 0.8×	29 1.2×	21 1.1×	12 0.7×	12 0.8×	10	265
Siyou Tan United States	9	376 1.5×	38 1.6×	26 1.4×	33 1.8×	31 2.1×	20	437
Malika Saint India	6	358 1.4×	22 0.9×	38 2.0×	10 0.6×	33 2.2×	6	383
Kavita A. Marfatia United States	7	365 1.4×	14 0.6×	11 0.6×	24 1.3×	17 1.1×	7	400
Sinje Geuer Germany	5	169 0.7×	60 2.5×	33 1.7×	8 0.4×	21 1.4×	6	215

Countries citing papers authored by Niels Jahn

Since Specialization

Citations

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

Fields of papers citing papers by Niels Jahn

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Niels Jahn

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

All Works

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10 of 10 papers shown

Kühlwein, Silke D., Johann M. Kraus, Julian Schwab, et al.. (2021). Digitalization of adverse event management in oncology to improve treatment outcome—A prospective study protocol. PLoS ONE. 16(6). e0252493–e0252493. 5 indexed citations

Schwab, Julian, Johannes Schobel, Nensi Ikonomi, et al.. (2021). Perspective on mHealth Concepts to Ensure Users’ Empowerment–From Adverse Event Tracking for COVID-19 Vaccinations to Oncological Treatment. IEEE Access. 9. 83863–83875. 5 indexed citations

Lausser, Ludwig, Klaus Huse, Niels Jahn, et al.. (2017). Tissue-, sex-, and age-specific DNA methylation of rat glucocorticoid receptor gene promoter and insulin-like growth factor 2 imprinting control region. Physiological Genomics. 49(11). 690–702. 13 indexed citations

Bens, Martin, Arne Sahm, Marco Groth, et al.. (2016). FRAMA: from RNA-seq data to annotated mRNA assemblies. BMC Genomics. 17(1). 54–54. 18 indexed citations

Sinha, Rileen, Thorsten Lenser, Niels Jahn, et al.. (2010). TassDB2 - A comprehensive database of subtle alternative splicing events. BMC Bioinformatics. 11(1). 216–216. 17 indexed citations

Sinha, Rileen, Swetlana Nikolajewa, Karol Szafranski, et al.. (2009). Accurate prediction of NAGNAG alternative splicing. Nucleic Acids Research. 37(11). 3569–3579. 24 indexed citations

Szafranski, Karol, Stefan Taudien, Michael Hiller, et al.. (2007). Violating the splicing rules: TG dinucleotides function as alternative 3' splice sites in U2-dependent introns. Genome biology. 8(8). R154–R154. 30 indexed citations

Hiller, Michael, Klaus Huse, Karol Szafranski, et al.. (2006). Single-Nucleotide Polymorphisms in NAGNAG Acceptors Are Highly Predictive for Variations of Alternative Splicing. The American Journal of Human Genetics. 78(2). 291–302. 44 indexed citations

Szafranski, Karol, Niels Jahn, & Matthias Platzer. (2006). tuple_plot: Fast pairwise nucleotide sequence comparison with noise suppression. Bioinformatics. 22(15). 1917–1918. 10 indexed citations

10.

Hiller, Michael, Klaus Huse, Karol Szafranski, et al.. (2004). Widespread occurrence of alternative splicing at NAGNAG acceptors contributes to proteome plasticity. Nature Genetics. 36(12). 1255–1257. 151 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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