Mark-A. Krogel
Impact in
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- Bayesian Modeling and Causal Inference
- Machine Learning and Data Classification
- Text and Document Classification Technologies
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- Computational Drug Discovery Methods
Papers in
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- Machine Learning in Bioinformatics 3
- Biomedical Text Mining and Ontologies 3
- Bioinformatics and Genomic Networks 2
- Gene expression and cancer classification 1
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- Data Mining Algorithms and Applications 3
- Co-authors
- Tobias Scheffer (4 shared papers)Jie Cheng (1 shared paper)Hisashi Hayashi (1 shared paper)Christos Hatzis (1 shared paper)Jun Sese (1 shared paper)Shinichi Morishita (1 shared paper)David Page (1 shared paper)Marco Landwehr (1 shared paper)
- Journals
- Machine Learning (1 paper)ACM SIGKDD Explorations Newsletter (2 papers)
In The Last Decade
Mark-A. Krogel
5 papers receiving 97 citations
Peers
Comparison fields: 5 of 31
- Artificial Intelligence 66
- Computational Theory and Mathematics 20
- Information Systems 23
- Signal Processing 10
- Computer Vision and Pattern Recognition 14
Countries citing papers authored by Mark-A. Krogel
This map shows the geographic impact of Mark-A. Krogel'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 Mark-A. Krogel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark-A. Krogel more than expected).
Fields of papers citing papers by Mark-A. Krogel
This network shows the impact of papers produced by Mark-A. Krogel. 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 Mark-A. Krogel. The network helps show where Mark-A. Krogel may publish in the future.
Co-authors
The 8 scholars most cited alongside Mark-A. Krogel, 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 | 2002 | 60 | |
| 2 | 2004 | 39 | |
| 3 | 2002 | 6 | |
| 4 | Effectiveness of information extraction, multi-relational, and multi-view learning for predicting gene deletion experiments | 2003 | 2 |
| 5 | 2004 | 1 |
About Mark-A. Krogel
Mark-A. Krogel is a scholar working on Molecular Biology, Information Systems, Artificial Intelligence, Infectious Diseases and Organic Chemistry, having authored 5 papers that have together received 108 indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (3 papers), Biomedical Text Mining and Ontologies (3 papers), Data Mining Algorithms and Applications (3 papers), Bioinformatics and Genomic Networks (2 papers), Machine Learning and Data Classification (1 paper), Machine Learning and Algorithms (1 paper), Bayesian Modeling and Causal Inference (1 paper) and Gene expression and cancer classification (1 paper). The work is most often cited by research in Artificial Intelligence (66 citations), Computational Theory and Mathematics (20 citations), Information Systems (23 citations), Signal Processing (10 citations) and Computer Vision and Pattern Recognition (14 citations). Mark-A. Krogel has collaborated with scholars based in Germany and Japan. Frequent co-authors include Tobias Scheffer, Jie Cheng, Hisashi Hayashi, Christos Hatzis, Jun Sese, Shinichi Morishita, David Page and Marco Landwehr. Their work appears in journals such as Machine Learning and ACM SIGKDD Explorations Newsletter.
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.