Mark-A. Krogel
Impact in
- Information Systems top 5%
- Data Mining Algorithms and Applications
- Artificial Intelligence top 10%
- Bayesian Modeling and Causal Inference
- Machine Learning and Data Classification
- Semantic Web and Ontologies
Papers in
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- Data Mining Algorithms and Applications 6
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- Biomedical Text Mining and Ontologies 3
- Machine Learning in Bioinformatics 3
- Bioinformatics and Genomic Networks 2
- Gene expression and cancer classification 1
- Co-authors
- Stefan Wrobel (3 shared papers)Tobias Scheffer (4 shared papers)Nada Lavrač (1 shared paper)Filip Železný (1 shared paper)Peter Flach (1 shared paper)David Page (1 shared paper)Shinichi Morishita (1 shared paper)Hisashi Hayashi (1 shared paper)
- Journals
- Machine Learning (1 paper)Lecture notes in computer science (3 papers)ACM SIGKDD Explorations Newsletter (2 papers)
In The Last Decade
Mark-A. Krogel
8 papers receiving 288 citations
Peers
Comparison fields: 5 of 43
- Information Systems 141
- Artificial Intelligence 200
- Computational Theory and Mathematics 95
- Signal Processing 59
- Computer Vision and Pattern Recognition 41
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 12 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 | 2003 | 99 | |
| 2 | 2002 | 82 | |
| 3 | 2001 | 79 | |
| 4 | 2004 | 47 | |
| 5 | 2002 | 6 | |
| 6 | 2002 | 3 | |
| 7 | 2004 | 2 | |
| 8 | Effectiveness of information extraction, multi-relational, and multi-view learning for predicting gene deletion experiments | 2003 | 2 |
About Mark-A. Krogel
Mark-A. Krogel is a scholar working on Information Systems, Molecular Biology, Artificial Intelligence, Computer Networks and Communications and Signal Processing, having authored 8 papers that have together received 320 indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (6 papers), Biomedical Text Mining and Ontologies (3 papers), Machine Learning in Bioinformatics (3 papers), Machine Learning and Algorithms (2 papers), Bioinformatics and Genomic Networks (2 papers), Semantic Web and Ontologies (1 paper), Data Management and Algorithms (1 paper) and Gene expression and cancer classification (1 paper). The work is most often cited by research in Information Systems (141 citations), Artificial Intelligence (200 citations), Computational Theory and Mathematics (95 citations), Signal Processing (59 citations) and Computer Vision and Pattern Recognition (41 citations). Mark-A. Krogel has collaborated with scholars based in Germany, Slovenia and Japan. Frequent co-authors include Stefan Wrobel, Tobias Scheffer, Nada Lavrač, Filip Železný, Peter Flach, David Page, Shinichi Morishita, Hisashi Hayashi, Christos Hatzis and Jun Sese. Their work appears in journals such as Machine Learning, Lecture notes in computer science 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.