Barbara Rakitsch
Impact in
- Genetics top 10%
- Genetic Mapping and Diversity in Plants and Animals
- Genetic and phenotypic traits in livestock
- Genetic Associations and Epidemiology
Papers in
-
- Gaussian Processes and Bayesian Inference 3
- Neural Networks and Applications 2
- Co-authors
- Oliver Stegle (5 shared papers)Karsten Borgwardt (5 shared papers)Christoph Lippert (3 shared papers)Francesco Paolo Casale (2 shared papers)Dominik G. Grimm (2 shared papers)Limin Li (1 shared paper)Eunyoung Chae (1 shared paper)Daniel Koenig (1 shared paper)
- Journals
- Bioinformatics (2 papers)Nature Methods (1 paper)PLoS Genetics (1 paper)PROTEOMICS (1 paper)Proceedings of the National Academy of Sciences (1 paper)
- Partner nations
- GermanyUnited KingdomSwitzerland
In The Last Decade
Barbara Rakitsch
14 papers receiving 342 citations
Peers
Comparison fields: 5 of 75
- Genetics 194
- Aging 12
- Plant Science 86
- Molecular Biology 112
- Horticulture 1
Countries citing papers authored by Barbara Rakitsch
This map shows the geographic impact of Barbara Rakitsch'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 Barbara Rakitsch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Barbara Rakitsch more than expected).
Fields of papers citing papers by Barbara Rakitsch
This network shows the impact of papers produced by Barbara Rakitsch. 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 Barbara Rakitsch. The network helps show where Barbara Rakitsch may publish in the future.
Co-authors
The 25 scholars most cited alongside Barbara Rakitsch, 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 | 2012 | 90 | |
| 2 | 2015 | 59 | |
| 3 | 2016 | 49 | |
| 4 | It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals | 2013 | 31 |
| 5 | 2016 | 29 | |
| 6 | 2018 | 24 | |
| 7 | 2011 | 24 | |
| 8 | 2016 | 14 | |
| 9 | 2017 | 13 | |
| 10 | 2024 | 9 | |
| 11 | 2018 | 3 | |
| 12 | 2023 | 2 | |
| 13 | Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes | 2020 | 1 |
| 14 | 2022 | 1 |
About Barbara Rakitsch
Barbara Rakitsch is a scholar working on Molecular Biology, Artificial Intelligence, Genetics, Control and Systems Engineering and Statistical and Nonlinear Physics, having authored 14 papers that have together received 349 indexed citations. Recurring topics across this work include Genetic Mapping and Diversity in Plants and Animals (3 papers), Gaussian Processes and Bayesian Inference (3 papers), Neural Networks and Applications (2 papers), Genetic and phenotypic traits in livestock (2 papers), Model Reduction and Neural Networks (2 papers), Genetic Associations and Epidemiology (2 papers), Genetic diversity and population structure (1 paper) and Fault Detection and Control Systems (1 paper). The work is most often cited by research in Genetics (194 citations), Aging (12 citations), Plant Science (86 citations), Molecular Biology (112 citations) and Horticulture (1 citation). Barbara Rakitsch has collaborated with scholars based in Germany, United Kingdom and Switzerland. Frequent co-authors include Oliver Stegle, Karsten Borgwardt, Christoph Lippert, Francesco Paolo Casale, Dominik G. Grimm, Limin Li, Eunyoung Chae, Daniel Koenig, Danelle K. Seymour and Anette Habring‐Müller. Their work appears in journals such as Bioinformatics, Nature Methods, PLoS Genetics, PROTEOMICS and Proceedings of the National Academy of Sciences.
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.