Petra Philips
Impact in
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- Machine Learning in Bioinformatics
- RNA and protein synthesis mechanisms
- RNA Research and Splicing
- Genomics and Phylogenetic Studies
- Genomics and Chromatin Dynamics
- Gene expression and cancer classification
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- Machine Learning and Data Classification
- Machine Learning and Algorithms
Papers in
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- Machine Learning and Algorithms 6
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- Sparse and Compressive Sensing Techniques 4
- Co-authors
- Gunnar Rätsch (3 shared papers)Sören Sonnenburg (3 shared papers)Gabriele Schweikert (2 shared papers)Jonas Behr (2 shared papers)Alexander Zien (2 shared papers)Shahar Mendelson (5 shared papers)Georg Zeller (1 shared paper)Nina Krüger (1 shared paper)
- Journals
- Bioinformatics (1 paper)Genome Research (1 paper)ESAIM Probability and Statistics (1 paper)Journal of Machine Learning Research (1 paper)BMC Bioinformatics (1 paper)
- Partner nations
- GermanyAustraliaUnited States
In The Last Decade
Petra Philips
8 papers receiving 286 citations
Peers
Comparison fields: 5 of 49
- Molecular Biology 201
- Artificial Intelligence 80
- Health Informatics 3
- Aging 3
- Computational Mathematics 1
Countries citing papers authored by Petra Philips
This map shows the geographic impact of Petra Philips'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 Petra Philips with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Petra Philips more than expected).
Fields of papers citing papers by Petra Philips
This network shows the impact of papers produced by Petra Philips. 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 Petra Philips. The network helps show where Petra Philips may publish in the future.
Co-authors
The 25 scholars most cited alongside Petra Philips, 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 | 2007 | 142 | |
| 2 | 2009 | 73 | |
| 3 | 2008 | 52 | |
| 4 | 2004 | 14 | |
| 5 | 2003 | 8 | |
| 6 | On the Importance of Small Coordinate Projections | 2004 | 3 |
| 7 | Optimal Sample-Based Estimates of the Expectation of the Empirical Minimizer | 2005 | 3 |
| 8 | Introduction to Statistical Learning Theory | 2007 | 3 |
| 9 | 2004 | 0 | |
| 10 | 2008 | 0 |
About Petra Philips
Petra Philips is a scholar working on Artificial Intelligence, Computational Mechanics, Statistics and Probability, Molecular Biology and Computational Theory and Mathematics, having authored 10 papers that have together received 298 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (6 papers), Sparse and Compressive Sensing Techniques (4 papers), Statistical Methods and Inference (3 papers), Computability, Logic, AI Algorithms (2 papers), Genomics and Phylogenetic Studies (1 paper), Face and Expression Recognition (1 paper), Distributed and Parallel Computing Systems (1 paper) and Particle Detector Development and Performance (1 paper). The work is most often cited by research in Molecular Biology (201 citations), Artificial Intelligence (80 citations), Health Informatics (3 citations), Aging (3 citations) and Computational Mathematics (1 citation). Petra Philips has collaborated with scholars based in Germany, Australia and United States. Frequent co-authors include Gunnar Rätsch, Sören Sonnenburg, Gabriele Schweikert, Jonas Behr, Alexander Zien, Shahar Mendelson, Georg Zeller, Nina Krüger, Christoph Dieterich and Peter L. Bartlett. Their work appears in journals such as Bioinformatics, Genome Research, ESAIM Probability and Statistics, Journal of Machine Learning Research and BMC Bioinformatics.
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.