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Karsten Borgwardt
- CV - Karsten Borgwardt
- In the news
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
Publications & Awards
- Publications
- Preprints
- Books
- Awards
  - Krupp Award 2013
- PhD theses