Conrad Plake
Impact in
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- Biomedical Text Mining and Ontologies
- Bioinformatics and Genomic Networks
- Machine Learning in Bioinformatics
- Genomics and Phylogenetic Studies
- Artificial Intelligence top 10%
- Semantic Web and Ontologies
- Topic Modeling
- Advanced Text Analysis Techniques
Papers in
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- Biomedical Text Mining and Ontologies 9
- Genomics and Phylogenetic Studies 3
- Bioinformatics and Genomic Networks 3
- Machine Learning in Bioinformatics 3
- Gene expression and cancer classification 1
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- Computational Drug Discovery Methods 2
- Co-authors
- Jörg Hakenberg (8 shared papers)Michael Schroeder (8 shared papers)Ulf Leser (5 shared papers)Graciela Gonzalez‐Hernandez (2 shared papers)Rainer Winnenburg (4 shared papers)Robert Leaman (1 shared paper)Löıc A. Royer (2 shared papers)Mark Schroeder (1 shared paper)
- Journals
- Bioinformatics (3 papers)BMC Bioinformatics (2 papers)Briefings in Bioinformatics (1 paper)Nucleic Acids Research (1 paper)Molecular & Cellular Proteomics (1 paper)
- Partner nations
- GermanyUnited StatesNetherlands
In The Last Decade
Conrad Plake
13 papers receiving 470 citations
Peers
Comparison fields: 5 of 62
- Molecular Biology 400
- Artificial Intelligence 165
- Computational Theory and Mathematics 45
- Aging 2
- Genetics 22
Countries citing papers authored by Conrad Plake
This map shows the geographic impact of Conrad Plake'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 Conrad Plake with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Conrad Plake more than expected).
Fields of papers citing papers by Conrad Plake
This network shows the impact of papers produced by Conrad Plake. 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 Conrad Plake. The network helps show where Conrad Plake may publish in the future.
Co-authors
The 25 scholars most cited alongside Conrad Plake, 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 | 2006 | 87 | |
| 2 | 2008 | 87 | |
| 3 | 2008 | 61 | |
| 4 | 2011 | 54 | |
| 5 | 2011 | 43 | |
| 6 | 2008 | 43 | |
| 7 | 2005 | 33 | |
| 8 | 2009 | 28 | |
| 9 | 2005 | 21 | |
| 10 | 2011 | 17 | |
| 11 | 2009 | 11 | |
| 12 | Mutation tagging with gene identifiers applied to membrane protein stability prediction. | 2008 | 2 |
| 13 | A Support Vector Machine classifier for gene name recognition | 2004 | 1 |
About Conrad Plake
Conrad Plake is a scholar working on Molecular Biology, Computational Theory and Mathematics, General Health Professions, Artificial Intelligence and Philosophy, having authored 13 papers that have together received 488 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (9 papers), Genomics and Phylogenetic Studies (3 papers), Bioinformatics and Genomic Networks (3 papers), Machine Learning in Bioinformatics (3 papers), Computational Drug Discovery Methods (2 papers), Hermeneutics and Narrative Identity (1 paper), Semantic Web and Ontologies (1 paper) and Gene expression and cancer classification (1 paper). The work is most often cited by research in Molecular Biology (400 citations), Artificial Intelligence (165 citations), Computational Theory and Mathematics (45 citations), Aging (2 citations) and Genetics (22 citations). Conrad Plake has collaborated with scholars based in Germany, United States and Netherlands. Frequent co-authors include Jörg Hakenberg, Michael Schroeder, Ulf Leser, Graciela Gonzalez‐Hernandez, Rainer Winnenburg, Robert Leaman, Löıc A. Royer, Mark Schroeder, Andreas Doms and Thomas Wächter. Their work appears in journals such as Bioinformatics, BMC Bioinformatics, Briefings in Bioinformatics, Nucleic Acids Research and Molecular & Cellular Proteomics.
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.