World's Best Scientists 2026 revealed!
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Computer Science
Switzerland
2025

D-Index & Metrics

Computer Science

D-Index
61
Citations
25968
World Ranking
2997
National Ranking
140

Research.com Recognitions

  • 2025 - Research.com Computer Science in Switzerland Leader Award
  • 2022 - Research.com Computer Science in Switzerland Leader Award

Overview

Karsten M. Borgwardt is affiliated with ETH Zurich in Switzerland and has contributed extensively to research across several interconnected fields. Their work spans biochemistry, genetics and molecular biology, medicine, and computer science, with a notable focus on molecular biology, artificial intelligence, epidemiology, genetics, and infectious diseases.

The scientist's research covers a range of topics, including sepsis diagnosis and treatment, machine learning in healthcare, bioinformatics and genomic networks, advanced graph neural networks, bacterial identification and susceptibility testing, gene expression and cancer classification, and genetic associations and epidemiology.

Karsten M. Borgwardt has published in numerous high-profile venues. The most frequent publication outlets include arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), Bioinformatics, Nature Communications, and Faculty Opinions - Post-Publication Peer Review of the Biomedical Literature.

Coauthor collaborations have been significant in their research, with frequent collaborators including Bastian Rieck, Dexiong Chen, Max Horn, Michael Moor, and Adrian Egli.

Selected recent papers by Karsten M. Borgwardt include:

  • Early prediction of circulatory failure in the intensive care unit using machine learning, 2020, Nature Medicine
  • Persistent complement dysregulation with signs of thromboinflammation in active Long Covid, 2024, Science
  • Biological network analysis with deep learning, 2020, Briefings in Bioinformatics
  • Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning, 2022, Nature Medicine
  • Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review, 2020, Clinical Microbiology and Infection

Best Publications

  • A kernel two-sample test

    Arthur Gretton;Karsten M. Borgwardt;Malte J. Rasch;Bernhard Schölkopf

  • A Kernel Method for the Two-Sample-Problem

    Arthur Gretton;Karsten M. Borgwardt;Malte Rasch;Bernhard Schölkopf

  • Correcting Sample Selection Bias by Unlabeled Data

    Jiayuan Huang;Arthur Gretton;Karsten M. Borgwardt;Bernhard Schölkopf

  • Weisfeiler-Lehman Graph Kernels

    Nino Shervashidze;Pascal Schweitzer;Erik Jan van Leeuwen;Kurt Mehlhorn

  • Integrating structured biological data by Kernel Maximum Mean Discrepancy

    Karsten M. Borgwardt;Arthur Gretton;Malte J. Rasch;Hans-Peter Kriegel

  • Protein function prediction via graph kernels

    Karsten M. Borgwardt;Cheng Soon Ong;Stefan Schönauer;S. V. N. Vishwanathan

  • Graph Kernels

    S. V. N. Vishwanathan;Nicol N. Schraudolph;Risi Kondor;Karsten M. Borgwardt

  • Shortest-path kernels on graphs

    K.M. Borgwardt;H.P. Kriegel

  • Efficient Graphlet Kernels for Large Graph Comparison

    Nino Sherashidze;S. V. N. Vishwanathan;Tobias H. Petri;Kurt Mehlhorn

  • Correcting sample selection bias by unlabeled data

    J Huang;AJ Smola;A Gretton;KM Borgwardt

  • Covariate Shift by Kernel Mean Matching

    A Gretton;AJ Smola;J Huang;M Schmittfull

  • Feature selection via dependence maximization

    Le Song;Alex Smola;Arthur Gretton;Justin Bedo

  • Supervised feature selection via dependence estimation

    Le Song;Alex Smola;Arthur Gretton;Karsten M. Borgwardt

  • Arabidopsis Defense against Botrytis cinerea: Chronology and Regulation Deciphered by High-Resolution Temporal Transcriptomic Analysis

    Oliver P. Windram;Priyadharshini Madhou;Stuart McHattie;Claire Hill

  • The Evaluation of Tools Used to Predict the Impact of Missense Variants Is Hindered by Two Types of Circularity

    Dominik G. Grimm;Dominik G. Grimm;Dominik G. Grimm;Chloé-Agathe Azencott;Fabian Aicheler;Fabian Aicheler;Udo Gieraths

  • Early prediction of circulatory failure in the intensive care unit using machine learning.

    Stephanie L. Hyland;Martin Faltys;Matthias Hüser;Matthias Hüser;Xinrui Lyu;Xinrui Lyu

  • Fast subtree kernels on graphs

    Nino Shervashidze;Karsten M. Borgwardt

  • Future trends in data mining

    Hans-Peter Kriegel;Karsten M. Borgwardt;Peer Kröger;Alexey Pryakhin

  • Biological network analysis with deep learning.

    Giulia Muzio;Leslie O'Bray;Karsten M. Borgwardt

  • Fast Computation of Graph Kernels

    Karsten M. Borgwardt;Nicol N. Schraudolph;S.v.n. Vishwanathan

  • Metropolis Algorithms for Representative Subgraph Sampling

    C. Hubler;H.-P. Kriegel;K. Borgwardt;Z. Ghahramani

  • An introduction to Gaussian processes

    O Stegle;KM Borgwardt

Frequent Co-Authors

Oliver Stegle
Oliver Stegle German Cancer Research Center
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Arthur Gretton
Arthur Gretton University College London
Detlef Weigel
Detlef Weigel Max Planck Institute for Developmental Biology
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Hans-Peter Kriegel
Hans-Peter Kriegel Ludwig-Maximilians-Universität München
Le Song
Le Song Mohamed bin Zayed University of Artificial Intelligence
S. V. N. Vishwanathan
S. V. N. Vishwanathan Purdue University West Lafayette
Gunnar Rätsch
Gunnar Rätsch ETH Zurich
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge

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