Ulf Leser

8.9k total citations · 3 hit papers
224 papers, 4.6k citations indexed

About

Ulf Leser is a scholar working on Molecular Biology, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Ulf Leser has authored 224 papers receiving a total of 4.6k indexed citations (citations by other indexed papers that have themselves been cited), including 120 papers in Molecular Biology, 98 papers in Artificial Intelligence and 52 papers in Computer Networks and Communications. Recurrent topics in Ulf Leser's work include Biomedical Text Mining and Ontologies (70 papers), Semantic Web and Ontologies (36 papers) and Topic Modeling (35 papers). Ulf Leser is often cited by papers focused on Biomedical Text Mining and Ontologies (70 papers), Semantic Web and Ontologies (36 papers) and Topic Modeling (35 papers). Ulf Leser collaborates with scholars based in Germany, United States and United Kingdom. Ulf Leser's co-authors include Jörg Hakenberg, Mariana Neves, Felix Naumann, Leon Weber, Tim Rocktäschel, Maryam Habibi, Philippe Thomas, Sebastian Wandelt, David Luis Wiegandt and Marc Bux and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and The Journal of Experimental Medicine.

In The Last Decade

Ulf Leser

216 papers receiving 4.4k citations

Hit Papers

align trajectories

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.

Deep learning with word embeddings improves biomedical named entity recognition

2017 356 citations Maryam Habibi, Leon Weber et al. Bioinformatics profile →
The Stratosphere platform for big data analytics

2014 258 citations Ulf Leser et al. profile →
Leveraging Large Language Models for Decision Support in Personalized Oncology

2023 139 citations Ulf Leser et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ulf Leser Germany	35	2.2k	2.0k	977	798	467	224	4.6k
Jaewoo Kang South Korea	37	4.9k 2.2×	2.9k 1.4×	804 0.8×	998 1.3×	528 1.1×	173	8.3k
Limsoon Wong Singapore	46	1.7k 0.8×	4.7k 2.4×	958 1.0×	749 0.9×	593 1.3×	303	7.8k
Natasha Noy United States	19	2.0k 0.9×	1.4k 0.7×	322 0.3×	792 1.0×	97 0.2×	57	2.9k
Keith C. C. Chan Hong Kong	39	1.3k 0.6×	1.5k 0.7×	451 0.5×	984 1.2×	283 0.6×	230	4.9k
Paolo Ferragina Italy	31	3.5k 1.6×	1.4k 0.7×	644 0.7×	668 0.8×	469 1.0×	135	4.8k
Michael R. Berthold Germany	25	1.2k 0.5×	1.1k 0.5×	272 0.3×	552 0.7×	344 0.7×	151	4.0k
Mario Cannataro Italy	29	739 0.3×	1.4k 0.7×	633 0.6×	626 0.8×	146 0.3×	244	3.4k
Nagiza F. Samatova United States	29	761 0.4×	1.3k 0.7×	820 0.8×	253 0.3×	194 0.4×	142	3.4k
Chun‐Nan Hsu Taiwan	26	1.2k 0.5×	871 0.4×	488 0.5×	639 0.8×	288 0.6×	103	2.6k
Reda Alhajj Canada	36	2.1k 1.0×	737 0.4×	995 1.0×	1.2k 1.5×	641 1.4×	404	5.1k

Countries citing papers authored by Ulf Leser

Since Specialization

Citations

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

Fields of papers citing papers by Ulf Leser

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ulf Leser

This figure shows the co-authorship network connecting the top 25 collaborators of Ulf Leser. A scholar is included among the top collaborators of Ulf Leser 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 Ulf Leser. Ulf Leser 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:

20 of 20 papers shown

1.

Sänger, Mario & Ulf Leser. (2025). Knowledge-augmented pre-trained language models for biomedical relation extraction. BMC Bioinformatics. 26(1). 240–240. 1 indexed citations

2.

Sänger, Mario, et al.. (2024). HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools. Bioinformatics. 40(10). 5 indexed citations

3.

Schintke, Florian, et al.. (2024). Validity constraints for data analysis workflows. Future Generation Computer Systems. 157. 82–97. 4 indexed citations

4.

Pourkarimi, Latif, et al.. (2023). A mathematical programming approach for resource allocation of data analysis workflows on heterogeneous clusters. The Journal of Supercomputing. 79(17). 19019–19048. 1 indexed citations

5.

Schäfer, Patrick, et al.. (2023). Window Size Selection in Unsupervised Time Series Analytics: A Review and Benchmark. Lecture notes in computer science. 83–101. 8 indexed citations

6.

Schäfer, Patrick & Ulf Leser. (2023). WEASEL 2.0: a random dilated dictionary transform for fast, accurate and memory constrained time series classification. Machine Learning. 112(12). 4763–4788. 19 indexed citations

7.

Weber, Leon, et al.. (2023). PEDL+: protein-centered relation extraction from PubMed at your fingertip. Bioinformatics. 39(11). 2 indexed citations

8.

Büttner, M., Ulf Leser, Lisa Schneider, & Falk Schwendicke. (2023). Natural Language Processing: Chances and Challenges in Dentistry. Journal of Dentistry. 141. 104796–104796. 33 indexed citations

9.

Weber, Leon, et al.. (2023). BELB: a biomedical entity linking benchmark. Bioinformatics. 39(11). 2 indexed citations

10.

Weber, Leon, et al.. (2022). Chemical–protein relation extraction with ensembles of carefully tuned pretrained language models. Database. 2022. 10 indexed citations

11.

Leser, Ulf, et al.. (2021). Early Detection of Sexual Predators in Chats. 4985–4999. 6 indexed citations

12.

Weber, Leon, et al.. (2020). Biomedical Event Extraction as Multi-turn Question Answering. 88–96. 19 indexed citations

13.

Habibi, Maryam, et al.. (2020). PatSeg: A Sequential Patent Segmentation Approach. Big Data Research. 19-20. 100133–100133. 4 indexed citations

14.

Kulbe, Hagen, Silvia Darb‐Esfahani, Hedwig Lammert, et al.. (2019). Discovery and Validation of Novel Biomarkers for Detection of Epithelial Ovarian Cancer. Cells. 8(7). 713–713. 32 indexed citations

15.

Bux, Marc, et al.. (2019). Predictive performance modeling for distributed batch processing using black box monitoring and machine learning. Information Systems. 82. 33–52. 40 indexed citations

16.

Habibi, Maryam, Leon Weber, Mariana Neves, David Luis Wiegandt, & Ulf Leser. (2017). Deep learning with word embeddings improves biomedical named entity recognition. Bioinformatics. 33(14). i37–i48. 356 indexed citations breakdown →

17.

Starlinger, Johannes, et al.. (2011). PiPa: custom integration of protein interactions and pathways. GI-Jahrestagung. 158. 2 indexed citations

18.

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

19.

Leser, Ulf & Peter Rieger. (2003). Integration molekularbiologischer Daten.. Datenbank-Spektrum. 6. 56–66. 1 indexed citations

20.

Leser, Ulf, et al.. (1999). Federated Information Systems: Concepts, Terminology and Architectures. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 38 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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