Mario Sänger
Impact in
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- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Machine Learning in Healthcare
- Sentiment Analysis and Opinion Mining
Papers in
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- Biomedical Text Mining and Ontologies 6
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- Topic Modeling 6
- Natural Language Processing Techniques 4
- Sentiment Analysis and Opinion Mining 3
- Advanced Text Analysis Techniques 1
- Co-authors
- Ulf Leser (11 shared papers)Leon Weber (4 shared papers)Roman Klinger (2 shared papers)Jurica Ševa (2 shared papers)Damian Rieke (1 shared paper)Maryam Habibi (1 shared paper)Mario Lamping (1 shared paper)Oliver Kohlbacher (1 shared paper)
- Journals
- Bioinformatics (2 papers)Database (1 paper)Language Resources and Evaluation (1 paper)BMC Bioinformatics (1 paper)GigaScience (1 paper)
- Partner nations
- GermanyAustriaUnited States
In The Last Decade
Mario Sänger
11 papers receiving 97 citations
Peers
Comparison fields: 5 of 23
- Artificial Intelligence 61
- Health Informatics 2
- Molecular Biology 50
- Health Information Management 3
- Information Systems and Management 4
Countries citing papers authored by Mario Sänger
This map shows the geographic impact of Mario Sänger'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 Mario Sänger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mario Sänger more than expected).
Fields of papers citing papers by Mario Sänger
This network shows the impact of papers produced by Mario Sänger. 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 Mario Sänger. The network helps show where Mario Sänger may publish in the future.
Co-authors
The 15 scholars most cited alongside Mario Sänger, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 30 | |
| 2 | 2022 | 11 | |
| 3 | Classifying German Animal Experiment Summaries with Multi-lingual BERT at CLEF eHealth 2019 Task 1. | 2019 | 9 |
| 4 | 2020 | 9 | |
| 5 | 2016 | 8 | |
| 6 | WBI at CLEF eHealth 2018 Task 1: Language-independent ICD-10 Coding using Multi-lingual Embeddings and Recurrent Neural Networks. | 2018 | 7 |
| 7 | 2024 | 7 | |
| 8 | 2017 | 7 | |
| 9 | 2024 | 6 | |
| 10 | 2021 | 6 | |
| 11 | 2025 | 1 |
About Mario Sänger
Mario Sänger is a scholar working on Molecular Biology, Artificial Intelligence, Information Systems, Information Systems and Management and Computational Theory and Mathematics, having authored 11 papers that have together received 101 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (6 papers), Topic Modeling (6 papers), Natural Language Processing Techniques (4 papers), Sentiment Analysis and Opinion Mining (3 papers), Scientific Computing and Data Management (1 paper), Computational Drug Discovery Methods (1 paper), Web Data Mining and Analysis (1 paper) and Advanced Text Analysis Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (61 citations), Health Informatics (2 citations), Molecular Biology (50 citations), Health Information Management (3 citations) and Information Systems and Management (4 citations). Mario Sänger has collaborated with scholars based in Germany, Austria and United States. Frequent co-authors include Ulf Leser, Leon Weber, Roman Klinger, Jurica Ševa, Damian Rieke, Maryam Habibi, Mario Lamping, Oliver Kohlbacher, Johannes Starlinger and Ulrich Keilholz. Their work appears in journals such as Bioinformatics, Database, Language Resources and Evaluation, BMC Bioinformatics and GigaScience.
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.