K. Borgwardt

发表

A Kernel Two-Sample Test

Bernhard Schölkopf, Alexander J. Smola, Arthur Gretton, 2012, J. Mach. Learn. Res..

Learning sparse inverse covariance matrices in the presence of confounders

K. Borgwardt, 2011, NIPS 2011.

A Kernel Method for the Two-Sample-Problem

Bernhard Schölkopf, Alexander J. Smola, Arthur Gretton, 2006, NIPS.

Covariate Shift by Kernel Mean Matching

Karsten M. Borgwardt, B. Schölkopf, Alex Smola, 2009, NIPS 2009.

Correcting Sample Selection Bias by Unlabeled Data

Bernhard Schölkopf, Alexander J. Smola, Arthur Gretton, 2006, NIPS.

Learning via Hilbert Space Embedding of Distributions

K. Fukumizu, Alex Smola, Y. Altun, 2007 .

Integrating structured biological data by Kernel Maximum Mean Discrepancy

Hans-Peter Kriegel, Bernhard Schölkopf, Alexander J. Smola, 2006, ISMB.

Feature Selection via Dependence Maximization

Le Song, Alexander J. Smola, Arthur Gretton, 2012, J. Mach. Learn. Res..

Kernelized rank learning for personalized drug recommendation

Karsten M. Borgwardt, Xiao He, Lukas Folkman, 2017, Bioinform..

Protein function prediction via graph kernels

Hans-Peter Kriegel, Alexander J. Smola, Cheng Soon Ong, 2005, ISMB.

Graph kernels

S. V. N. Vishwanathan, Nicol N. Schraudolph, Karsten M. Borgwardt, 2007 .

A Kernel Approach to Comparing Distributions

Bernhard Schölkopf, Alexander J. Smola, Arthur Gretton, 2007, AAAI.

A dependence maximization view of clustering

Le Song, Alexander J. Smola, Arthur Gretton, 2007, ICML '07.

Arabidopsis Defense against Botrytis cinerea: Chronology and Regulation Deciphered by High-Resolution Temporal Transcriptomic Analysis[C][W][OA]

Christopher A. Penfold, Emma J. Cooke, Dafyd J. Jenkins, 2012, Plant Cell.

ShapePheno: unsupervised extraction of shape phenotypes from biological image collections

Daniel P. Miranker, Oliver Stegle, Karsten M. Borgwardt, 2012, Bioinform..

Gene selection via the BAHSIC family of algorithms

Le Song, Alexander J. Smola, Arthur Gretton, 2007, ISMB/ECCB.

Discriminative frequent subgraph mining with optimality guarantees

Philip S. Yu, Hans-Peter Kriegel, Le Song, 2010, Stat. Anal. Data Min..

Colored Maximum Variance Unfolding

Le Song, Alexander J. Smola, Arthur Gretton, 2007, NIPS.

Supervised feature selection via dependence estimation

Le Song, Alexander J. Smola, Arthur Gretton, 2007, ICML '07.

Bayesian two-sample tests pdf

Zoubin Ghahramani, Karsten M. Borgwardt, Zoubin Ghahramani, 2009, ArXiv.

A kernel method for unsupervised structured network inference

Zoubin Ghahramani, Oliver Stegle, Karsten M. Borgwardt, 2009, AISTATS.

Spontaneous epigenetic variation in the Arabidopsis thaliana methylome

Karsten M. Borgwardt, K. Borgwardt, O. Stegle, 2011, Nature.

Whole-genome sequencing of multiple Arabidopsis thaliana populations

K. Borgwardt, C. Lippert, O. Stegle, 2011, Nature Genetics.

Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping

Anja C. Gumpinger, Y. Benenson, K. Borgwardt, 2020, Nature Communications.

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

Karsten M. Borgwardt, M. Daly, D. MacArthur, 2015, Human mutation.

1,135 Genomes Reveal the Global Pattern of Polymorphism in Arabidopsis thaliana

Karsten M. Borgwardt, Stefan R. Henz, Robert J. Schmitz, 2016, Cell.

1 Covariate Shift by Kernel Mean Matching

B. Schölkopf, Alex Smola, A. Gretton, 2008 .

Accurate indel prediction using paired-end short reads

K. Borgwardt, D. Weigel, D. Grimm, 2013, BMC Genomics.

EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units

B. Schölkopf, K. Borgwardt, A. Ziegler, 2011, European Journal of Human Genetics.

Enhancing statistical power in temporal biomarker discovery through representative shapelet mining

K. Borgwardt, Bastian Alexander Rieck, Thomas Gumbsch, 2020, Bioinformatics.

Epigenomic Diversity in a Global Collection of Arabidopsis thaliana Accessions

Stefan R. Henz, Robert J. Schmitz, K. Borgwardt, 2016, Cell.

Finding Significant Combinations of Continuous Features

K. Borgwardt, M. Sugiyama, 2017 .

Association mapping in biomedical time series via statistically significant shapelet mining

Karsten M. Borgwardt, Thomas Gumbsch, Christian Bock, 2018, Bioinform..

OP-CBIO170071 1820..1828

K. Borgwardt, Damian Roqueiro, Laetitia Papaxanthos, 2017 .

easyGWAS: A Cloud-Based Platform for Comparing the Results of Genome-Wide Association Studies[OPEN]

Bernhard Schölkopf, Karsten M. Borgwardt, Christoph Lippert, 2016, Plant Cell.

Efficient network-guided multi-locus association mapping with graph cuts

Yoshinobu Kawahara, Karsten M. Borgwardt, Dominik Grimm, 2012, Bioinform..

Genetic architecture of nonadditive inheritance in Arabidopsis thaliana hybrids

Detlef Weigel, Barbara Rakitsch, Karsten M Borgwardt, 2016, Proceedings of the National Academy of Sciences.

GLIDE: GPU-Based Linear Regression for Detection of Epistasis

B. Schölkopf, K. Borgwardt, A. Altmann, 2012, Human Heredity.

A min-cut solution to mapping phenotypes to networks of genetic markers

K. Borgwardt, Y. Kawahara, D. Grimm, 2012 .

AraPheno and the AraGWAS Catalog 2020: a major database update including RNA-Seq and knockout mutation data for Arabidopsis thaliana

K. Borgwardt, M. Nordborg, D. Weigel, 2019, Nucleic Acids Res..

Weisfeiler and Leman go Machine Learning: The Story so far pdf

Yaron Lipman, Nils M. Kriege, Martin Grohe, 2021, ArXiv.

Graph Kernels: State-of-the-Art and Future Challenges

K. Borgwardt, F. Llinares-López, B. Rieck, 2020 .

Kernel Algorithms Distribution Embeddings in Reproducing Kernel Hilbert Spaces

B. Schölkopf, K. Fukumizu, Alex Smola, 2009 .

Bootstrat : Population Informed Bootstrapping for Rare Variant 1 Tests 2 3

M. Daly, K. Borgwardt, B. Neale, 2016 .

Topological Methods for fMRI Data

K. Borgwardt, N. Turk-Browne, Guy Wolf, 2020 .

Spatio-Spectral Remote Sensing Image Classification With Graph Kernels

Gustavo Camps-Valls, Karsten M. Borgwardt, Nino Shervashidze, 2010, IEEE Geoscience and Remote Sensing Letters.

Aberrant working memory processing in major depression: evidence from multivoxel pattern classification

Karsten M. Borgwardt, K. Borgwardt, S. Grimm, 2018, Neuropsychopharmacology.

Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning

K. Borgwardt, Bastian Alexander Rieck, Maximilian Brackmann, 2020, bioRxiv.

Comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatment strategies, and outcomes in adult and pediatric patients with COVID-19: A systematic review and meta-analysis

Karsten M. Borgwardt, K. Borgwardt, Bastian Alexander Rieck, 2020, Travel Medicine and Infectious Disease.

Structure-Aware Transformer for Graph Representation Learning

Karsten M. Borgwardt, K. Borgwardt, Leslie O’Bray, 2022, ICML.

Geometric tree kernels

Marleen de Bruijne, K. Borgwardt, A. Dirksen, 2017 .

Genomic Profiles of Diversification and Genotype-Phenotype Association in Island Nematode Lineages.

R. Sommer, K. Borgwardt, C. Rödelsperger, 2016, Molecular biology and evolution.

A kernel method to comparing distributions

Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, 2007, AAAI 2007.

A mixed model approach for joint genetic analysis of alternatively spliced transcript isoforms using RNA-Seq data

K. Borgwardt, A. Honkela, C. Lippert, 2012, 1210.2850.

Approximate Network Motif Mining Via Graph Learning

K. Borgwardt, Vincent Mallet, Dexiong Chen, 2022, ArXiv.

Unsupervised Manifold Alignment with Joint Multidimensional Scaling

K. Borgwardt, Dexiong Chen, Carlos G. Oliver, 2022, ArXiv.

Multiple output regression with latent noise. Journal of Machine Learning

M. Pirinen, K. Borgwardt, Samuel Kaski, 2016 .

E arly P rediction of S epsis in the ICU using M achine L earning : A S ystematic R eview .

K. Borgwardt, C. Jutzeler, Michael Moor, 2020 .

The magnitude vector of images

Karsten M. Borgwardt, Michael F. Adamer, K. Borgwardt, 2021, ArXiv.

RNA atlas of human bacterial pathogens uncovers stress dynamics linked to infection

K. Borgwardt, Jing Tang, K. Avican, 2020, Nature Communications.

Weisfeiler and Leman go Machine Learning: The Story so far

Nils M. Kriege, K. Borgwardt, Y. Lipman, 2021, ArXiv.

An Introduction to Graph Mining

Oliver Stegle, Karsten M. Borgwardt, K. Borgwardt, 2010 .

Oliver Stegle, Karsten M. Borgwardt, Christoph Lippert, 2009, GCB 2009.

An introduction to Gaussian processes

Oliver Stegle, Karsten M. Borgwardt, K. Borgwardt, 2010 .

An Introduction to Probabilistic modeling

Oliver Stegle, Karsten M. Borgwardt, Oliver Stegle, 2010 .

Advanced graph kernels: Graphlet Kernels

Nino Shervashidze, Kurt Mehlhorn, Karsten M. Borgwardt, 2010 .

Machine learning for early prediction of circulatory failure in the intensive care unit pdf

Gunnar Rätsch, Karsten M. Borgwardt, Marc Zimmermann, 2019, ArXiv.

Accurate indel prediction using paired-end short reads

K. Borgwardt, D. Weigel, D. Grimm, 2013, BMC Genomics.

Representation Learning for Dynamic Graphs: A Survey

Karsten M. Borgwardt, Seyed Mehran Kazemi, K. Borgwardt, 2019, J. Mach. Learn. Res..

PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning

Karsten Borgwardt, Jannis Born, Matteo Manica, 2019 .

reComBat: batch-effect removal in large-scale multi-source gene-expression data integration

Michael F. Adamer, K. Borgwardt, Marek Basler, 2021, bioRxiv.

Pretransplant Kinetics of Anti-HLA Antibodies in Patients on the Waiting List for Kidney Transplantation.

K. Borgwardt, D. Yoneoka, A. Kolios, 2019, Journal of the American Society of Nephrology.

Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs

Karsten M. Borgwardt, Tony Kam-Thong, Benno Pütz, 2011, Bioinform..

Determinants of SARS-CoV-2 transmission to guide vaccination strategy in an urban area

K. Borgwardt, M. Battegay, A. Buser, 2022, Virus evolution.

Determinants of SARS-CoV-2 transmission to guide vaccination strategy in a city

K. Borgwardt, M. Battegay, A. Buser, 2020, medRxiv.

Kernel Methods in Bioinformatics

Karsten M. Borgwardt, K. Borgwardt, 2011, Handbook of Statistical Bioinformatics.

Kernel extrapolation

Alexander J. Smola, S. V. N. Vishwanathan, Karsten M. Borgwardt, 2006, Neurocomputing.

Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence

Karsten M. Borgwardt, K. Borgwardt, N. Turk-Browne, 2020, NeurIPS.

Weisfeiler-Lehman Graph Kernels

Kurt Mehlhorn, Erik Jan van Leeuwen, Karsten M. Borgwardt, 2011, J. Mach. Learn. Res..

Shortest-path kernels on graphs

Hans-Peter Kriegel, Karsten M. Borgwardt, K. Borgwardt, 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

A Persistent Weisfeiler-Lehman Procedure for Graph Classification

Karsten M. Borgwardt, Christian Bock, Bastian Rieck, 2019, ICML.

Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping

Karsten M. Borgwardt, Christian Beisel, Yaakov Benenson, 2020, Nature Communications.

Biological network analysis with deep learning

Karsten M. Borgwardt, Leslie O'Bray, Giulia Muzio, 2020, Briefings Bioinform..

Graph Kernels: State-of-the-Art and Future Challenges pdf

Karsten Borgwardt, Bastian Rieck, Elisabetta Ghisu, 2020, Found. Trends Mach. Learn..

Downregulating α-synuclein in iPSC-derived dopaminergic neurons mimics electrophysiological phenotype of the A53T mutation

Manuel S. Schröter, K. Borgwardt, M. Fiscella, 2022, bioRxiv.

Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts

Gunnar Rätsch, Karsten M. Borgwardt, Regina Bohnert, 2010, ISMB 2011.

Methods and Tools in Genome-wide Association Studies.

Anja C Gumpinger, Damian Roqueiro, Dominik G Grimm, 2018, Methods in molecular biology.

Multi-target drug repositioning by bipartite block-wise sparse multi-task learning

Karsten M. Borgwardt, Xiao He, Limin Li, 2018, BMC Syst. Biol..

Faculty Opinions recommendation of Panning for gold: ‘model‐X’ knockoffs for high dimensional controlled variable selection.

Karsten M. Borgwardt, Karsten Borgwardt, K. Borgwardt, 2019, Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature.

Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis pdf

Karsten M. Borgwardt, Gabriel Brändle, Nina Wiedemann, 2020, ArXiv.

Prediction of human population responses to toxic compounds by a collaborative competition

Ana L. Teixeira, Eleni G. Christodoulou, Jeffrey T. Chang, 2015, Nature Biotechnology.

Kernel conditional clustering and kernel conditional semi-supervised learning

Thomas Gumbsch, Karsten Borgwardt, Xiao He, 2019, Knowledge and Information Systems.

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

Karsten M. Borgwardt, Stephanie L. Hyland, Martin Faltys, 2020, Nature Medicine.

Set Functions for Time Series

Christian Bock, Michael Moor, Karsten Borgwardt, 2019, ICML.

Century-scale Methylome Stability in a Recently Diverged Arabidopsis thaliana Lineage

Karsten M. Borgwardt, K. Borgwardt, O. Stegle, 2014, bioRxiv.

Genome-wide detection of intervals of genetic heterogeneity associated with complex traits

Karsten M. Borgwardt, Dominik Grimm, Mahito Sugiyama, 2015, Bioinform..

Accelerating detection of lung pathologies with explainable ultrasound image analysis

Karsten M. Borgwardt, Michael Moor, Jannis Born, 2021 .

networkGWAS: a network-based approach to discover genetic associations

K. Borgwardt, Leslie O’Bray, Laetitia Meng-Papaxanthos, 2023, bioRxiv.

networkGWAS: A network-based approach for genome-wide association studies in structured populations

K. Borgwardt, G. Muzio, Leslie O’Bray, 2021 .

Ethnicity-based bias in clinical severity scores.

K. Borgwardt, Thomas Gumbsch, 2021, The Lancet. Digital health.

The AraGWAS Catalog: a curated and standardized Arabidopsis thaliana GWAS catalog

Karsten M. Borgwardt, Dominik Grimm, Detlef Weigel, 2017, Nucleic Acids Res..

AraPheno: a public database for Arabidopsis thaliana phenotypes

Karsten M. Borgwardt, Dominik Grimm, Detlef Weigel, 2016, Nucleic Acids Res..

Appendix to Supervised Feature Selection via Dependence Estimation

Alex Smola, Le Song, A. Gretton, 2006 .

The BAHSIC family of gene selection algorithms

Arthur Gretton, Karsten M. Borgwardt, Justin Bedo, 2006 .

Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review

Karsten M. Borgwardt, K. Borgwardt, Bastian Alexander Rieck, 2020, Frontiers in Medicine.

Prediction of cancer driver genes through network-based moment propagation of mutation scores

Karsten Borgwardt, Heiko Horn, Kasper Lage, 2020, Bioinform..

Accurate and adaptive imputation of summary statistics in mixed-ethnicity cohorts

Karsten M. Borgwardt, Damian Roqueiro, Matteo Togninalli, 2018, Bioinform..

A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series

Zoubin Ghahramani, Oliver Stegle, Karsten M. Borgwardt, 2009, RECOMB.

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

Stephanie L. Hyland, G. Rätsch, K. Borgwardt, 2020, Nature Medicine.

Accurate detection of differential RNA processing

Gunnar Rätsch, André Kahles, Karsten Borgwardt, 2013, Nucleic acids research.

SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research

Gunnar Rätsch, Karsten Borgwardt, Peter Bühlmann, 2020, MIE.

Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra

Karsten Borgwardt, Adrian Egli, Bastian Rieck, 2020, Bioinform..

Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review

Karsten M. Borgwardt, Caroline V. Weis, Catherine R. Jutzeler, 2020 .

Temporal Convolutional Networks and Dynamic Time Warping can Drastically Improve the Early Prediction of Sepsis pdf

Karsten M. Borgwardt, Max Horn, Damian Roqueiro, 2019, ArXiv.

Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping

Karsten Borgwardt, Max Horn, Damian Roqueiro, 2019, MLHC.

Discovering Temporal Patterns of Differential Gene Expression in Microarray Time Series

Zoubin Ghahramani, Andrew Meade, Oliver Stegle, 2009, GCB.

Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning

K. Borgwardt, Bastian Alexander Rieck, Maximilian Brackmann, 2020, Nature Medicine.

A Lasso multi-marker mixed model for association mapping with population structure correction

Oliver Stegle, Karsten M. Borgwardt, Christoph Lippert, 2013, Bioinform..

Efficient graphlet kernels for large graph comparison

Kurt Mehlhorn, S. V. N. Vishwanathan, Karsten M. Borgwardt, 2009, AISTATS.

Multi-Task Feature Selection on Multiple Networks via Maximum Flows

Yoshinobu Kawahara, Karsten M. Borgwardt, Dominik Grimm, 2014, SDM.

Frequent subgraph discovery in dynamic networks

Christian Böhm, Karsten M. Borgwardt, Annahita Oswald, 2010, MLG '10.

Pattern Mining in Frequent Dynamic Subgraphs

Hans-Peter Kriegel, Karsten M. Borgwardt, Peter Wackersreuther, 2006, Sixth International Conference on Data Mining (ICDM'06).

It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals

Oliver Stegle, Karsten M. Borgwardt, Christoph Lippert, 2013, NIPS.

Searching for significant patterns in stratified data pdf

Karsten M. Borgwardt, Felipe Llinares-López, Dean A. Bodenham, 2015, ArXiv.

Detecting regulatory gene-environment interactions with unmeasured environmental factors

Neil D. Lawrence, Oliver Stegle, Karsten M. Borgwardt, 2013, Bioinform..

Weisfeiler and Leman go Machine Learning: The Story so far

Nils M. Kriege, K. Borgwardt, Y. Lipman, 2021, ArXiv.

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning pdf

Michael Moor, Karsten Borgwardt, Max Horn, 2021, ArXiv.

Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions

Karsten M. Borgwardt, K. Borgwardt, Bastian Alexander Rieck, 2021, ICLR.

Topological Graph Neural Networks

K. Borgwardt, Y. Moreau, Bastian Alexander Rieck, 2021, ICLR.

Topological Autoencoders pdf

Karsten Borgwardt, Max Horn, Bastian Rieck, 2019, ICML.

Improved MALDI-TOF MS based antimicrobial resistance prediction through hierarchical stratification

K. Borgwardt, A. Egli, C. Weis, 2022, bioRxiv.

Kernel Conditional Clustering

Karsten M. Borgwardt, Thomas Gumbsch, Xiao He, 2017, 2017 IEEE International Conference on Data Mining (ICDM).

Rapid Distance-Based Outlier Detection via Sampling

Karsten M. Borgwardt, Mahito Sugiyama, K. Borgwardt, 2013, NIPS.

Scalable kernels for graphs with continuous attributes

Marleen de Bruijne, Jens Petersen, Karsten M. Borgwardt, 2013, NIPS.

Future trends in data mining

Hans-Peter Kriegel, Peer Kröger, Arthur Zimek, 2007, Data Mining and Knowledge Discovery.

Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning

K. Borgwardt, Bastian Alexander Rieck, Maximilian Brackmann, 2022, Nature Medicine.

Kernel Methods for Protein Function Prediction

Karsten M. Borgwardt, I PeterKriegel, K. Borgwardt, 2005 .

Near-optimal Supervised Feature Selection among Frequent Subgraphs

Philip S. Yu, Hans-Peter Kriegel, Le Song, 2009, SDM.

Scalable covariance-based connectivity inference for synchronous neuronal networks

Karsten M. Borgwardt, Manuel S. Schröter, Dexiong Chen, 2023, bioRxiv.

Filtration Curves for Graph Representation

K. Borgwardt, Bastian Alexander Rieck, Leslie O’Bray, 2021, KDD.

A Wasserstein Subsequence Kernel for Time Series

Karsten M. Borgwardt, Thomas Gumbsch, Christian Bock, 2019, 2019 IEEE International Conference on Data Mining (ICDM).

ArabidopsisDefense againstBotrytis cinerea: Chronology and Regulation Deciphered by High-Resolution Temporal Transcriptomic AnalysisC

Christopher A. Penfold, Emma J. Cooke, Dafyd J. Jenkins, 2013 .

Metropolis Algorithms for Representative Subgraph Sampling

Hans-Peter Kriegel, Zoubin Ghahramani, Karsten M. Borgwardt, 2008, 2008 Eighth IEEE International Conference on Data Mining.

Fisher Information Embedding for Node and Graph Learning

K. Borgwardt, Dexiong Chen, Paolo Pellizzoni, 2023, ICML.

Fast Computation of Graph Kernels

S. V. N. Vishwanathan, Nicol N. Schraudolph, Karsten M. Borgwardt, 2006, NIPS.

Using routine MRI data of depressed patients to predict individual responses to electroconvulsive therapy

K. Borgwardt, I. Heuser, C. Otte, 2020, Experimental Neurology.

Halting in Random Walk Kernels

Karsten M. Borgwardt, Mahito Sugiyama, K. Borgwardt, 2015, NIPS.

Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning

Christian Bock, Michael Moor, Karsten Borgwardt, 2021, Artificial Neural Networks, 3rd Edition.

Graph Kernels For Disease Outcome Prediction From Protein-Protein Interaction Networks

Hans-Peter Kriegel, Karsten M. Borgwardt, K. Borgwardt, 2006, Pacific Symposium on Biocomputing.

Weisfeiler and Leman go Machine Learning: The Story so far

Nils M. Kriege, K. Borgwardt, Y. Lipman, 2021, ArXiv.

Efficient inference in matrix-variate Gaussian models with \iid observation noise

Neil D. Lawrence, Joris M. Mooij, Oliver Stegle, 2011, NIPS.

Fast subtree kernels on graphs

Karsten M. Borgwardt, Nino Shervashidze, K. Borgwardt, 2009, NIPS.

The graphlet spectrum

Karsten M. Borgwardt, Risi Kondor, Nino Shervashidze, 2009, ICML '09.

Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology pdf

Karsten M. Borgwardt, Thomas Gumbsch, Max Horn, 2018, ICLR.

Graph Kernels

Karsten M. Borgwardt, I. Kondor, R. Kondor, 2008, J. Mach. Learn. Res..

graphkernels: R and Python packages for graph comparison

Karsten M. Borgwardt, Mahito Sugiyama, Felipe Llinares-López, 2017, Bioinform..

Geometric Tree Kernels: Classification of COPD from Airway Tree Geometry

Marleen de Bruijne, K. Borgwardt, A. Dirksen, 2013, IPMI.

The skew spectrum of graphs

Karsten M. Borgwardt, Risi Kondor, R. Kondor, 2008, ICML '08.

Class Prediction from Time Series Gene Expression Profiles Using Dynamical Systems Kernels

Hans-Peter Kriegel, S. V. N. Vishwanathan, Karsten M. Borgwardt, 2005, Pacific Symposium on Biocomputing.

An Efficient Sampling Scheme For Comparison of Large Graphs

Hans-Peter Kriegel, S. V. N. Vishwanathan, Karsten M. Borgwardt, 2007, MLG.

Joint Regularization

Alexander J. Smola, S. V. N. Vishwanathan, Karsten M. Borgwardt, 2005, ESANN.

Finding Statistically Significant Interactions between Continuous Features

Karsten M. Borgwardt, Mahito Sugiyama, K. Borgwardt, 2017, IJCAI.

Significant Pattern Mining on Continuous Variables pdf

Karsten M. Borgwardt, Mahito Sugiyama, K. Borgwardt, 2017, ArXiv.

Significant Subgraph Mining with Multiple Testing Correction pdf

Karsten M. Borgwardt, Niklas Kasenburg, Mahito Sugiyama, 2014, SDM.

Multiple Testing Correction in Graph Mining

Karsten M. Borgwardt, Niklas Kasenburg, Llinares López, 2015 .

Machine Learning in Medicine

K. Borgwardt, R. Deo, C. Jutzeler, 2015, Mach. Learn. under Resour. Constraints Vol. 3.

Gene function prediction from synthetic lethality networks via ranking on demand

Zoubin Ghahramani, Karsten M. Borgwardt, Christoph Lippert, 2010, Bioinform..

P ITFALLS , AND P RACTICAL S OLUTIONS

K. Borgwardt, Bastian Alexander Rieck, Leslie O’Bray, 2022 .

ccSVM: correcting Support Vector Machines for confounding factors in biological data classification

Karsten M. Borgwardt, Limin Li, Barbara Rakitsch, 2011, Bioinform..

Direct Antimicrobial Resistance Prediction from MALDI-TOF mass spectra profile in clinical isolates through Machine Learning

K. Borgwardt, Bastian Alexander Rieck, A. Egli, 2020 .

The genome of the simian and human malaria parasite Plasmodium knowlesi

Thomas M. Keane, Irmtraud M. Meyer, M. Simmonds, 2008, Nature.

Bootstrat: Population Informed Bootstrapping for Rare Variant Tests

Karsten M. Borgwardt, Hailiang Huang, Daniel P. Howrigan, 2016, bioRxiv.

Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing

Karsten M. Borgwardt, Mahito Sugiyama, Felipe Llinares-López, 2015, KDD.

Graph Mining and Graph Kernels

Xifeng Yan, Karsten Michael Borgwardt, K. Borgwardt, 2008, KDD 2008.

Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics

K. Borgwardt, Francisco Pinto, R. Singh, 2023, Bioinformatics.

Higher-order genetic interaction discovery with network-based biological priors

K. Borgwardt, Paolo Pellizzoni, Giulia Muzio, 2023, Bioinformatics.

Network-guided search for genetic heterogeneity between gene pairs

Karsten M. Borgwardt, Dominik G. Grimm, Anja C. Gumpinger, 2020, Bioinform..

Predicting sepsis using deep learning across international sites: a retrospective development and validation study

N. Meinshausen, P. Bühlmann, K. Borgwardt, 2023, EClinicalMedicine.

Genome-wide genetic heterogeneity discovery with categorical covariates

Karsten M. Borgwardt, Felipe Llinares-López, Dean A. Bodenham, 2017, Bioinform..

Finding significant combinations of features in the presence of categorical covariates

Karsten M. Borgwardt, Felipe Llinares-López, Dean A. Bodenham, 2016, NIPS.

Knowledge Discovery in Databases

M. Jay Norton, K. Borgwardt, A. Zimek, 1999, Libr. Trends.

Machine Learning in Medicine

Rahul C. Deo, K. Borgwardt, C. Jutzeler, 2015, Mach. Learn. under Resour. Constraints Vol. 3.

easyGWAS: An integrated interspecies platform for performing genome-wide association studies pdf

Bernhard Schölkopf, Oliver Stegle, Karsten M. Borgwardt, 2012, ArXiv.

Supervised feature selection via dependence estimation

Alex Smola, Le Song, A. Gretton, 2007, ICML '07.

Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis

K. Borgwardt, A. Aujayeb, Michael Moor, 2020, Applied Sciences.