Sören Sonnenburg - Concepedia

Author

Sören Sonnenburg

Max Planck Society

Also Known As

Sonnenburg, Soeren, Sonnenburg, Sören, Soren Sonnenburg, Sören Sonnenburg

Publications

3.9K

Citations

H-Index

Concepts

All Affiliations

Max Planck Society Friedrich Miescher Laboratory Fraunhofer-Gesellschaft Max Planck Institute for Biological Cybernetics Technische Universität Berlin

About

Sören Sonnenburg is an author at Max Planck Society specializing in machine learning, engineering, and knowledge discovery.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	6	6	502
2	Data Science	5	5	402
3	Medicine	3	3	232
4	Computer Science	3	3	100
5	Biostatistics	2	2	318

Sören Sonnenburg

Publications

2.7K

Citations

H-Index

Page 1

	Year	Citations
Large Scale Multiple Kernel Learning Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer, MPG.PuRe (Max Planck Society)	2006	1.2K
Efficient and Accurate Lp-Norm Multiple Kernel Learning Marius Kloft, Ulf Brefeld, Pavel Laskov, Multiple KernelsSystems BiologyEngineeringMachine LearningData Science	2009	218
Accurate splice site prediction using support vector machines Sören Sonnenburg, Gabriele Schweikert, Petra Philips, BMC Bioinformatics	2007	187
A General and Efficient Multiple Kernel Learning Algorithm Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer Max Planck Institute for Plasma Physics	2005	147
ARTS: accurate recognition of transcription starts in human Sören Sonnenburg, Alexander Zien, Gunnar Rätsch Bioinformatics	2006	136
A New Discriminative Kernel from Probabilistic Models Koji Tsuda, Motoaki Kawanabe, Gunnar Rätsch, Neural Computation	2002	129
Learning Interpretable SVMs for Biological Sequence Classification Gunnar Rätsch, Sören Sonnenburg, Christin Schäfer BMC Bioinformatics	2006	106
mGene: Accurate SVM-based gene finding with an application to nematode genomes Gabriele Schweikert, Alexander Zien, Georg Zeller, Genome Research EngineeringMachine LearningGeneticsGenomicsGene Recognition	2009	100
Classifying ‘Drug-likeness' with Kernel-Based Learning Methods Klaus‐Robert Müller, Gunnar Rätsch, Sören Sonnenburg, Journal of Chemical Information and Modeling EngineeringMachine LearningHit IdentificationDrug ClassSupport Vector Machine	2005	84
Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies Bettina Mieth, Marius Kloft, Juan Antonio Rodríguez, Scientific Reports	2016	80

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