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Karsten Borgwardt
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Lab Members
Research
Machine Learning
Graph Kernels
graphkernels: R and Python packages for graph comparison
Halting in Random Walk Kernels
Scalable kernels for graphs with continuos attributes
Weisfeiler-Lehman Graph Kernels
Graph Kernels
Graph Kernels Survey
Significant Pattern Mining
Nonlinear Measures of Statistical Dependence (Maximum Information Dimension)
Rapid Outlier Detection
Kernel Method for the Two Sample Problem
Confounder-corrected Classification with Support Vector Machines
Significant Pattern Mining (Westfall-Young Light)
Significant Pattern Mining with Covariates (FACS)
Multi-view Spectral Clustering on Conflicting Views
Kernel Conditional Clustering
Bioinformatics and Computational Biology
Epistasis tools
easyGWAS
LMM-Lasso
SConES
Multi-SConES
Structural Variant Machine (SV-M)
Protein Function Prediction via Graph Kernels
Pathogenicity Prediction
Multi-Locus Mapping of Genetic Heterogeneity (FAIS)
In silico Phenotyping
Genetic Heterogeneity Discovery (FastCMH)
all-GWAS: Virtual Machine
Combinatorial Association Mapping
Personalized Medicine
Machine Learning for Personalized Medicine
Personalized Swiss Sepsis Study
Machine Learning Frontiers in Precision Medicine
Teaching
Data Mining I
Data Mining II
Student thesis
Previous courses
Tutorials and workshops
Tutorial at ISMB 2018
Workshop at D-BSSE retreat 2019
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Krupp Award 2013
PhD theses
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