Sören Sonnenburg

3.9k total citations · 2 hit papers
20 papers, 2.4k citations indexed

About

Sören Sonnenburg is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Sören Sonnenburg has authored 20 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 9 papers in Artificial Intelligence and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Sören Sonnenburg's work include Machine Learning in Bioinformatics (8 papers), Gene expression and cancer classification (7 papers) and Face and Expression Recognition (6 papers). Sören Sonnenburg is often cited by papers focused on Machine Learning in Bioinformatics (8 papers), Gene expression and cancer classification (7 papers) and Face and Expression Recognition (6 papers). Sören Sonnenburg collaborates with scholars based in Germany, United States and Norway. Sören Sonnenburg's co-authors include Gunnar Rätsch, Bernhard Schölkopf, Christin Schäfer, Cheng Soon Ong, Asa Ben‐Hur, Alexander Zien, Klaus‐Robert Müller, Petra Philips, Marius Kloft and Ulf Brefeld and has published in prestigious journals such as Bioinformatics, PLoS ONE and Scientific Reports.

In The Last Decade

Sören Sonnenburg

20 papers receiving 2.3k citations

Hit Papers

Large Scale Multiple Kernel Learning 2006 2026 2012 2019 2006 2008 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Large Scale Multiple Kernel Learning
    2006 853 citations Sören Sonnenburg, Gunnar Rätsch et al. Journal of Machine Learning Research profile →
  2. Support Vector Machines and Kernels for Computational Biology
    2008 501 citations Asa Ben‐Hur, Cheng Soon Ong et al. PLoS Computational Biology profile →

Peers

Sören Sonnenburg
S. V. N. Vishwanathan United States
Douglas E. Zongker United States
Hua Yu China
Nicol N. Schraudolph Switzerland
Dao‐Qing Dai China
Christos Boutsidis United States
Chun-Hou Zheng China
Thomas Villmann Germany
Martin Heusel Austria
David Page United States
S. V. N. Vishwanathan United States
Sören Sonnenburg
Citations per year, relative to Sören Sonnenburg Sören Sonnenburg (= 1×) peers S. V. N. Vishwanathan

Countries citing papers authored by Sören Sonnenburg

Since Specialization
Citations

This map shows the geographic impact of Sören Sonnenburg'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 Sören Sonnenburg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sören Sonnenburg more than expected).

Fields of papers citing papers by Sören Sonnenburg

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sören Sonnenburg. 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 Sören Sonnenburg. The network helps show where Sören Sonnenburg may publish in the future.

Co-authorship network of co-authors of Sören Sonnenburg

This figure shows the co-authorship network connecting the top 25 collaborators of Sören Sonnenburg. A scholar is included among the top collaborators of Sören Sonnenburg based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sören Sonnenburg. Sören Sonnenburg is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kloft, Marius, Juan Antonio Rodríguez, Sören Sonnenburg, et al.. (2016). Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies. Scientific Reports. 6(1). 36671–36671. 47 indexed citations
2.
Jenssen, Robert, Marius Kloft, Alexander Zien, Sören Sonnenburg, & Klaus‐Robert Müller. (2012). A Scatter-Based Prototype Framework and Multi-Class Extension of Support Vector Machines. PLoS ONE. 7(10). e42947–e42947. 7 indexed citations
3.
Jenssen, Robert, Marius Kloft, Sören Sonnenburg, Alexander Zien, & Klaus‐Robert Müller. (2011). A new scatter-based multi-class support vector machine. 6. 1–6. 1 indexed citations
4.
Widmer, Christian, et al.. (2011). Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation. 24. 2690–2698. 23 indexed citations
5.
Kloft, Marius, Ulf Brefeld, Sören Sonnenburg, & Alexander Zien. (2010). Non-Sparse Regularization and Efficient Training with Multiple Kernels. UC Berkeley. 25 indexed citations
6.
Jenssen, Robert, Marius Kloft, Alexander Zien, Sören Sonnenburg, & Klaus‐Robert Müller. (2009). A Multi-Class Support Vector Machine Based on Scatter Criteria. DepositOnce. 3 indexed citations
7.
Schweikert, Gabriele, Alexander Zien, Georg Zeller, et al.. (2009). mGene: Accurate SVM-based gene finding with an application to nematode genomes. Genome Research. 19(11). 2133–2143. 61 indexed citations
8.
Franc, Vojtěch & Sören Sonnenburg. (2009). Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization. Max Planck Institute for Plasma Physics. 10(76). 2157–2192. 43 indexed citations
9.
Kloft, Marius, Ulf Brefeld, Pavel Laskov, et al.. (2009). Efficient and Accurate Lp-Norm Multiple Kernel Learning. 22. 997–1005. 151 indexed citations
10.
Ben‐Hur, Asa, Cheng Soon Ong, Sören Sonnenburg, Bernhard Schölkopf, & Gunnar Rätsch. (2008). Support Vector Machines and Kernels for Computational Biology. PLoS Computational Biology. 4(10). e1000173–e1000173. 501 indexed citations breakdown →
11.
Sonnenburg, Sören, Alexander Zien, Petra Philips, & Gunnar Rätsch. (2008). POIMs: positional oligomer importance matrices—understanding support vector machine-based signal detectors. Bioinformatics. 24(13). i6–i14. 45 indexed citations
12.
Sonnenburg, Sören, Gabriele Schweikert, Petra Philips, Jonas Behr, & Gunnar Rätsch. (2007). Accurate splice site prediction using support vector machines. BMC Bioinformatics. 8(S10). S7–S7. 128 indexed citations
13.
Rätsch, Gunnar, et al.. (2007). Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning. PLoS Computational Biology. 3(2). e20–e20. 36 indexed citations
14.
Sonnenburg, Sören, Gunnar Rätsch, Christin Schäfer, & Bernhard Schölkopf. (2006). Large Scale Multiple Kernel Learning. Journal of Machine Learning Research. 7(57). 1531–1565. 853 indexed citations breakdown →
15.
Rätsch, Gunnar, Sören Sonnenburg, & Christin Schäfer. (2006). Learning Interpretable SVMs for Biological Sequence Classification. BMC Bioinformatics. 7(S1). S9–S9. 68 indexed citations
16.
Sonnenburg, Sören, Alexander Zien, & Gunnar Rätsch. (2006). ARTS: accurate recognition of transcription starts in human. Bioinformatics. 22(14). e472–e480. 85 indexed citations
17.
Sonnenburg, Sören, Gunnar Rätsch, & Bernhard Schölkopf. (2005). Large scale genomic sequence SVM classifiers. 848–855. 35 indexed citations
18.
Müller, Klaus‐Robert, Gunnar Rätsch, Sören Sonnenburg, et al.. (2005). Classifying ‘Drug-likeness' with Kernel-Based Learning Methods. Journal of Chemical Information and Modeling. 45(2). 249–253. 65 indexed citations
19.
Sonnenburg, Sören, Gunnar Rätsch, & Christin Schäfer. (2005). A General and Efficient Multiple Kernel Learning Algorithm. Max Planck Institute for Plasma Physics. 18. 1273–1280. 108 indexed citations
20.
Tsuda, Koji, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, & Klaus‐Robert Müller. (2002). A New Discriminative Kernel from Probabilistic Models. Neural Computation. 14(10). 2397–2414. 74 indexed citations

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.

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