Joint Regularization. - Karsten M. Borgwardt; Omri Guttman; S. V. N. Vishwanathan; Alexander J. Smola (ESANN, 2005)

Shortest-Path Kernels on Graphs. - Karsten M. Borgwardt; Hans-Peter Kriegel (ICDM, 2005)

Protein function prediction via graph kernels. - Karsten M. Borgwardt; Cheng Soon Ong; Stefan Schönauer; S. V. N. Vishwanathan; Alexander J. Smola; Hans-Peter Kriegel (ISMB (Supplement of Bioinformatics), 2005)

3DString: a feature string kernel for 3D object classification on voxelized data. - Johannes Aßfalg; Karsten M. Borgwardt; Hans-Peter Kriegel (CIKM, 2006)

Pattern Mining in Frequent Dynamic Subgraphs. - Karsten M. Borgwardt; Hans-Peter Kriegel; Peter Wackersreuther (ICDM, 2006)

Integrating structured biological data by Kernel Maximum Mean Discrepancy. - Karsten M. Borgwardt; Arthur Gretton; Malte J. Rasch; Hans-Peter Kriegel; Bernhard Schölkopf; Alexander J. Smola (ISMB (Supplement of Bioinformatics), 2006)

A Kernel Method for the Two-Sample-Problem. - Arthur Gretton; Karsten M. Borgwardt; Malte J. Rasch; Bernhard Schölkopf; Alexander J. Smola (NIPS, 2006)

Correcting Sample Selection Bias by Unlabeled Data. - Jiayuan Huang; Alexander J. Smola; Arthur Gretton; Karsten M. Borgwardt; Bernhard Schölkopf (NIPS, 2006)

Fast Computation of Graph Kernels. - S. V. N. Vishwanathan; Karsten M. Borgwardt; Nicol N. Schraudolph (NIPS, 2006)

Class Prediction from Time Series Gene Expression Profiles Using Dynamical Systems Kernels. - Karsten M. Borgwardt; S. V. N. Vishwanathan; Hans-Peter Kriegel (Pacific Symposium on Biocomputing, 2006)

VGM: visual graph mining. - Karsten M. Borgwardt; Sebastian Böttger; Hans-Peter Kriegel (SIGMOD Conference, 2006)

Kernel extrapolation. - S. V. N. Vishwanathan; Karsten M. Borgwardt; Omri Guttman; Alexander J. Smola (Neurocomputing, 2006)

A Kernel Approach to Comparing Distributions. - Arthur Gretton; Karsten M. Borgwardt; Malte J. Rasch; Bernhard Schölkopf; Alexander J. Smola (AAAI, 2007)

Graph-Kerne [Graph Kernels]. - Karsten M. Borgwardt (Ausgezeichnete Informatikdissertationen, 2007)

A dependence maximization view of clustering. - Le Song; Alexander J. Smola; Arthur Gretton; Karsten M. Borgwardt (ICML, 2007)

Supervised feature selection via dependence estimation. - Le Song; Alexander J. Smola; Arthur Gretton; Karsten M. Borgwardt; Justin Bedo (ICML, 2007)

Gene selection via the BAHSIC family of algorithms. - Le Song; Justin Bedo; Karsten M. Borgwardt; Arthur Gretton; Alexander J. Smola (ISMB/ECCB (Supplement of Bioinformatics), 2007)

An Efficient Sampling Scheme For Comparison of Large Graphs. - Karsten M. Borgwardt; Tobias Petri; S. V. N. Vishwanathan; Hans-Peter Kriegel (MLG, 2007)

Colored Maximum Variance Unfolding. - Le Song; Alexander J. Smola; Karsten M. Borgwardt; Arthur Gretton (NIPS, 2007)

Graph Kernels For Disease Outcome Prediction From Protein-Protein Interaction Networks. - Karsten M. Borgwardt; Hans-Peter Kriegel (Pacific Symposium on Biocomputing, 2007)

Future trends in data mining. - Hans-Peter Kriegel; Karsten M. Borgwardt; Peer Kröger; Alexey Pryakhin; Matthias Schubert; Arthur Zimek (Data Min. Knowl. Discov., 2007)

Graph kernels. - Karsten Michael Borgwardt (2007)

Metropolis Algorithms for Representative Subgraph Sampling. - Christian Hübler; Hans-Peter Kriegel; Karsten M. Borgwardt; Zoubin Ghahramani (ICDM, 2008)

The skew spectrum of graphs. - Risi Kondor; Karsten M. Borgwardt (ICML, 2008)

Combining near-optimal feature selection with gSpan. - Karsten M. Borgwardt; Xifeng Yan; Marisa Thoma; Hong Cheng 001; Arthur Gretton; Le Song; Alex Smola; Jiawei Han 001; Philip S. Yu; Hans-Peter Kriegel (MLG@ICML, 2008)

Representative Subgraph Sampling using Markov Chain Monte Carlo Methods. - Christian Hübler; Karsten M. Borgwardt; Hans-Peter Kriegel; Zoubin Ghahramani (MLG@ICML, 2008)

Discovering Temporal Patterns of Differential Gene Expression in Microarray Time Series. - Oliver Stegle; Katherine J. Denby; David L. Wild; Stuart McHattie; Andrew Meade; Zoubin Ghahramani; Karsten M. Borgwardt (GCB, 2009)

The graphlet spectrum. - Risi Kondor; Nino Shervashidze; Karsten M. Borgwardt (ICML, 2009)

Fast subtree kernels on graphs. - Nino Shervashidze; Karsten M. Borgwardt (NIPS, 2009)

A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series. - Oliver Stegle; Katherine J. Denby; David L. Wild; Zoubin Ghahramani; Karsten M. Borgwardt (RECOMB, 2009)

Near-optimal Supervised Feature Selection among Frequent Subgraphs. - Marisa Thoma; Hong Cheng 001; Arthur Gretton; Jiawei Han 001; Hans-Peter Kriegel; Alexander J. Smola; Le Song; Philip S. Yu; Xifeng Yan; Karsten M. Borgwardt (SDM, 2009)

Bayesian two-sample tests - Karsten M. Borgwardt; Zoubin Ghahramani (CoRR, 2009)

A kernel method for unsupervised structured network inference. - Christoph Lippert; Oliver Stegle; Zoubin Ghahramani; Karsten M. Borgwardt (AISTATS, 2009)

Efficient graphlet kernels for large graph comparison. - Nino Shervashidze; S. V. N. Vishwanathan; Tobias Petri; Kurt Mehlhorn; Karsten M. Borgwardt (AISTATS, 2009)

Frequent subgraph discovery in dynamic networks. - Bianca Wackersreuther; Peter Wackersreuther; Annahita Oswald; Christian Böhm 001; Karsten M. Borgwardt (MLG@KDD, 2010)

Gene function prediction from synthetic lethality networks via ranking on demand. - Christoph Lippert; Zoubin Ghahramani; Karsten M. Borgwardt (Bioinform., 2010)

A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series. - Oliver Stegle; Katherine J. Denby; Emma J. Cooke; David L. Wild; Zoubin Ghahramani; Karsten M. Borgwardt (J. Comput. Biol., 2010)

Graph Kernels. - S. V. N. Vishwanathan; Nicol N. Schraudolph; Risi Kondor; Karsten M. Borgwardt (J. Mach. Learn. Res., 2010)

Spatio-Spectral Remote Sensing Image Classification With Graph Kernels. - Gustavo Camps-Valls; Nino Shervashidze; Karsten M. Borgwardt (IEEE Geosci. Remote. Sens. Lett., 2010)

Discriminative frequent subgraph mining with optimality guarantees. - Marisa Thoma; Hong Cheng 001; Arthur Gretton; Jiawei Han 001; Hans-Peter Kriegel; Alexander J. Smola; Le Song; Philip S. Yu; Xifeng Yan; Karsten M. Borgwardt (Stat. Anal. Data Min., 2010)

Kernel Methods in Bioinformatics. - Karsten M. Borgwardt (Handbook of Statistical Bioinformatics, 2011)

Two-locus association mapping in subquadratic time. - Panagiotis Achlioptas; Bernhard Schölkopf; Karsten M. Borgwardt (KDD, 2011)

Efficient inference in matrix-variate Gaussian models with iid observation noise. - Oliver Stegle; Christoph Lippert; Joris M. Mooij; Neil D. Lawrence; Karsten M. Borgwardt (NIPS, 2011)

Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs. - Tony Kam-Thong; Benno Pütz; Nazanin Karbalai; Bertram Müller-Myhsok; Karsten M. Borgwardt (Bioinform., 2011)

ccSVM: correcting Support Vector Machines for confounding factors in biological data classification. - Limin Li; Barbara Rakitsch; Karsten M. Borgwardt (Bioinform., 2011)

Efficient branch-and-bound techniques for two-locus association mapping. - Karin Klotzbücher; Yasushi Kobayashi; Nino Shervashidze; Oliver Stegle; Bertram Müller-Myhsok; Detlef Weigel; Karsten M. Borgwardt (BMC Bioinform., 2011)

Weisfeiler-Lehman Graph Kernels. - Nino Shervashidze; Pascal Schweitzer; Erik Jan van Leeuwen; Kurt Mehlhorn; Karsten M. Borgwardt (J. Mach. Learn. Res., 2011)

Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning. - Hendrik Blockeel; Karsten M. Borgwardt; Luc De Raedt; Pedro M. Domingos; Kristian Kersting; Xifeng Yan (Mach. Learn., 2011)

ShapePheno: unsupervised extraction of shape phenotypes from biological image collections. - Theofanis Karaletsos; Oliver Stegle; Christine Dreyer; John M. Winn; Karsten M. Borgwardt (Bioinform., 2012)

easyGWAS: An integrated interspecies platform for performing genome-wide association studies - Dominik G. Grimm; Bastian Greshake; Stefan Kleeberger; Christoph Lippert; Oliver Stegle; Bernhard Schölkopf; Detlef Weigel; Karsten M. Borgwardt (CoRR, 2012)

A Kernel Two-Sample Test. - Arthur Gretton; Karsten M. Borgwardt; Malte J. Rasch; Bernhard Schölkopf; Alexander J. Smola (J. Mach. Learn. Res., 2012)

Feature Selection via Dependence Maximization. - Le Song; Alexander J. Smola; Arthur Gretton; Justin Bedo; Karsten M. Borgwardt (J. Mach. Learn. Res., 2012)

Measuring Statistical Dependence via the Mutual Information Dimension. - Mahito Sugiyama; Karsten M. Borgwardt (IJCAI, 2013)

Geometric Tree Kernels: Classification of COPD from Airway Tree Geometry. - Aasa Feragen; Jens Petersen; Dominik G. Grimm; Asger Dirksen; Jesper Johannes Holst Pedersen; Karsten M. Borgwardt; Marleen de Bruijne (IPMI, 2013)

Scalable kernels for graphs with continuous attributes. - Aasa Feragen; Niklas Kasenburg; Jens Petersen; Marleen de Bruijne; Karsten M. Borgwardt (NIPS, 2013)

It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals. - Barbara Rakitsch; Christoph Lippert; Karsten M. Borgwardt; Oliver Stegle (NIPS, 2013)

Rapid Distance-Based Outlier Detection via Sampling. - Mahito Sugiyama; Karsten M. Borgwardt (NIPS, 2013)

Efficient network-guided multi-locus association mapping with graph cuts. - Chloé-Agathe Azencott; Dominik G. Grimm; Mahito Sugiyama; Yoshinobu Kawahara; Karsten M. Borgwardt (Bioinform., 2013)

Detecting regulatory gene-environment interactions with unmeasured environmental factors. - Nicoló Fusi; Christoph Lippert; Karsten M. Borgwardt; Neil D. Lawrence; Oliver Stegle (Bioinform., 2013)

A Lasso multi-marker mixed model for association mapping with population structure correction. - Barbara Rakitsch; Christoph Lippert; Oliver Stegle; Karsten M. Borgwardt (Bioinform., 2013)

Multi-Task Feature Selection on Multiple Networks via Maximum Flows. - Mahito Sugiyama; Chloé-Agathe Azencott; Dominik G. Grimm; Yoshinobu Kawahara; Karsten M. Borgwardt (SDM, 2014)

Identifying Higher-order Combinations of Binary Features. - Felipe Llinares; Mahito Sugiyama; Karsten M. Borgwardt (CoRR, 2014)

Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing. - Felipe Llinares-López; Mahito Sugiyama; Laetitia Papaxanthos; Karsten M. Borgwardt (KDD, 2015)

Halting in Random Walk Kernels. - Mahito Sugiyama; Karsten M. Borgwardt (NIPS, 2015)

Significant Subgraph Mining with Multiple Testing Correction. - Mahito Sugiyama; Felipe Llinares-López; Niklas Kasenburg; Karsten M. Borgwardt (SDM, 2015)

Genome-wide detection of intervals of genetic heterogeneity associated with complex traits. - Felipe Llinares-López; Dominik G. Grimm; Dean A. Bodenham; Udo Gieraths; Mahito Sugiyama; Beth Rowan; Karsten M. Borgwardt (Bioinform., 2015)

In silico phenotyping via co-training for improved phenotype prediction from genotype. - Damian Roqueiro; Menno J. Witteveen; Verneri Anttila; Gisela M. Terwindt; Arn M. J. M. van den Maagdenberg; Karsten M. Borgwardt (Bioinform., 2015)

Searching for significant patterns in stratified data. - Felipe Llinares-López; Laetitia Papaxanthos; Dean A. Bodenham; Karsten M. Borgwardt (CoRR, 2015)

Finding significant combinations of features in the presence of categorical covariates. - Laetitia Papaxanthos; Felipe Llinares-López; Dean A. Bodenham; Karsten M. Borgwardt (NIPS, 2016)

Kernel Conditional Clustering. - Xiao He 008; Thomas Gumbsch; Damian Roqueiro; Karsten M. Borgwardt (ICDM, 2017)

Multi-view Spectral Clustering on Conflicting Views. - Xiao He 008; Limin Li; Damian Roqueiro; Karsten M. Borgwardt (ECML/PKDD (2), 2017)

Genome-wide genetic heterogeneity discovery with categorical covariates. - Felipe Llinares-López; Laetitia Papaxanthos; Dean A. Bodenham; Damian Roqueiro; COPDGene Investigators; Karsten M. Borgwardt (Bioinform., 2017)

Significant Pattern Mining on Continuous Variables. - Mahito Sugiyama; Karsten M. Borgwardt (CoRR, 2017)

AraPheno: a public database for Arabidopsis thaliana phenotypes. - Ümit Seren; Dominik G. Grimm; Joffrey Fitz; Detlef Weigel; Magnus Nordborg; Karsten M. Borgwardt; Arthur Korte (Nucleic Acids Res., 2017)

Association mapping in biomedical time series via statistically significant shapelet mining. - Christian Bock; Thomas Gumbsch; Michael Moor; Bastian Rieck; Damian Roqueiro; Karsten M. Borgwardt (Bioinform., 2018)

Kernelized rank learning for personalized drug recommendation. - Xiao He 008; Lukas Folkman; Karsten M. Borgwardt (Bioinform., 2018)

graphkernels: R and Python packages for graph comparison. - Mahito Sugiyama; M. Elisabetta Ghisu; Felipe Llinares-López; Karsten M. Borgwardt (Bioinform., 2018)

Accurate and adaptive imputation of summary statistics in mixed-ethnicity cohorts. - Matteo Togninalli; Damian Roqueiro; COPDGene Investigators; Karsten M. Borgwardt (Bioinform., 2018)

Multi-target drug repositioning by bipartite block-wise sparse multi-task learning. - Limin Li; Xiao He 008; Karsten M. Borgwardt (BMC Syst. Biol., 2018)

The AraGWAS Catalog: a curated and standardized Arabidopsis thaliana GWAS catalog. - Matteo Togninalli; Ümit Seren; Dazhe Meng; Joffrey Fitz; Magnus Nordborg; Detlef Weigel; Karsten M. Borgwardt; Arthur Korte; Dominik G. Grimm (Nucleic Acids Res., 2018)

A Wasserstein Subsequence Kernel for Time Series. - Christian Bock; Matteo Togninalli; M. Elisabetta Ghisu; Thomas Gumbsch; Bastian Rieck; Karsten M. Borgwardt (ICDM, 2019)

Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology. - Bastian Rieck; Matteo Togninalli; Christian Bock; Michael Moor; Max Horn; Thomas Gumbsch; Karsten M. Borgwardt (ICLR (Poster), 2019)

A Persistent Weisfeiler-Lehman Procedure for Graph Classification. - Bastian Rieck; Christian Bock; Karsten M. Borgwardt (ICML, 2019)

Finding Statistically Significant Interactions between Continuous Features. - Mahito Sugiyama; Karsten M. Borgwardt (IJCAI, 2019)

Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping. - Michael Moor; Max Horn; Bastian Rieck; Damian Roqueiro; Karsten M. Borgwardt (MLHC, 2019)

Wasserstein Weisfeiler-Lehman Graph Kernels. - Matteo Togninalli; M. Elisabetta Ghisu; Felipe Llinares-López; Bastian Rieck; Karsten M. Borgwardt (NeurIPS, 2019)

CASMAP: detection of statistically significant combinations of SNPs in association mapping. - Felipe Llinares-López; Laetitia Papaxanthos; Damian Roqueiro; Dean A. Bodenham; Karsten M. Borgwardt (Bioinform., 2019)

Temporal Convolutional Networks and Dynamic Time Warping can Drastically Improve the Early Prediction of Sepsis. - Michael Moor; Max Horn; Bastian Rieck; Damian Roqueiro; Karsten M. Borgwardt (CoRR, 2019)

Machine learning for early prediction of circulatory failure in the intensive care unit. - Stephanie L. Hyland; Martin Faltys; Matthias Hüser; Xinrui Lyu; Thomas Gumbsch; Cristóbal Esteban; Christian Bock; Max Horn; Michael Moor; Bastian Rieck; Marc Zimmermann; Dean A. Bodenham; et al. (CoRR, 2019)

Introduction to the special issue for the ECML PKDD 2019 journal track. - Karsten M. Borgwardt; Po-Ling Loh; Evimaria Terzi; Antti Ukkonen (Mach. Learn., 2019)

Set Functions for Time Series. - Max Horn; Michael Moor; Christian Bock; Bastian Rieck; Karsten M. Borgwardt (ICML, 2020)

Topological Autoencoders. - Michael Moor; Max Horn; Bastian Rieck; Karsten M. Borgwardt (ICML, 2020)

SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research. - Adrian Egli; Manuel Battegay; Andrea C. Büchler; Peter Bühlmann; Thierry Calandra; Philippe Eckert; Hansjakob Furrer; Gilbert Greub; Stephan M. Jakob; Laurent Kaiser; Stephen L. Leib; Stephan Marsch; et al. (MIE, 2020)

Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence. - Bastian Rieck; Tristan Yates; Christian Bock; Karsten M. Borgwardt; Guy Wolf; Nicholas B. Turk-Browne; Smita Krishnaswamy (NeurIPS, 2020)

Enhancing statistical power in temporal biomarker discovery through representative shapelet mining. - Thomas Gumbsch; Christian Bock; Michael Moor; Bastian Rieck; Karsten M. Borgwardt (Bioinform., 2020)

Prediction of cancer driver genes through network-based moment propagation of mutation scores. - Anja C. Gumpinger; Kasper Lage; Heiko Horn; Karsten M. Borgwardt (Bioinform., 2020)

Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra. - Caroline Weis; Max Horn; Bastian Rieck; Aline Cuénod; Adrian Egli; Karsten M. Borgwardt (Bioinform., 2020)

Path Imputation Strategies for Signature Models. - Michael Moor; Max Horn; Christian Bock; Karsten M. Borgwardt; Bastian Rieck (CoRR, 2020)

Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis. - Jannis Born; Nina Wiedemann; Gabriel Brändle; Charlotte Buhre; Bastian Rieck; Karsten M. Borgwardt (CoRR, 2020)

Graph Kernels: State-of-the-Art and Future Challenges. - Karsten M. Borgwardt; M. Elisabetta Ghisu; Felipe Llinares-López; Leslie O'Bray; Bastian Rieck (Found. Trends Mach. Learn., 2020)

Kernel conditional clustering and kernel conditional semi-supervised learning. - Xiao He 008; Thomas Gumbsch; Damian Roqueiro; Karsten M. Borgwardt (Knowl. Inf. Syst., 2020)

AraPheno and the AraGWAS Catalog 2020: a major database update including RNA-Seq and knockout mutation data for Arabidopsis thaliana. - Matteo Togninalli; Ümit Seren; Jan A. Freudenthal; J. Grey Monroe; Dazhe Meng; Magnus Nordborg; Detlef Weigel; Karsten M. Borgwardt; Arthur Korte; Dominik G. Grimm (Nucleic Acids Res., 2020)

Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning. - Christian Bock; Michael Moor; Catherine R. Jutzeler; Karsten M. Borgwardt (Artificial Neural Networks, 3rd Edition, 2021)

Filtration Curves for Graph Representation. - Leslie O'Bray; Bastian Rieck; Karsten M. Borgwardt (KDD, 2021)

Biological network analysis with deep learning. - Giulia Muzio; Leslie O'Bray; Karsten M. Borgwardt (Briefings Bioinform., 2021)

Network-guided search for genetic heterogeneity between gene pairs. - Anja C. Gumpinger; Bastian Rieck; Dominik G. Grimm; Karsten M. Borgwardt (Bioinform., 2021)

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning. - Michael Moor; Nicolas Bennett; Drago Plecko; Max Horn; Bastian Rieck; Nicolai Meinshausen; Peter Bühlmann; Karsten M. Borgwardt (CoRR, 2021)

The magnitude vector of images. - Michael F. Adamer; Leslie O'Bray; Edward De Brouwer; Bastian Rieck; Karsten M. Borgwardt (CoRR, 2021)

Machine Learning in Medicine. - Catherine R. Jutzeler; Karsten M. Borgwardt (Mach. Learn. under Resour. Constraints Vol. 3 (3), 2022)

Topological Graph Neural Networks. - Max Horn; Edward De Brouwer; Michael Moor; Yves Moreau; Bastian Rieck; Karsten M. Borgwardt (ICLR, 2022)

Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions. - Leslie O'Bray; Max Horn; Bastian Rieck; Karsten M. Borgwardt (ICLR, 2022)

Structure-Aware Transformer for Graph Representation Learning. - Dexiong Chen; Leslie O'Bray; Karsten M. Borgwardt (ICML, 2022)

Prediction of recovery from multiple organ dysfunction syndrome in pediatric sepsis patients. - Bowen Fan; Juliane Klatt; Michael Moor; Latasha A. Daniels; Philipp K. A Agyeman; Christoph Berger; Eric Giannoni; Martin Stocker; Klara M. Posfay-Barbe; Ulrich Heininger; Sara Bernhard-Stirnemann; Anita Niederer-Loher; et al. (Bioinform., 2022)

Approximate Network Motif Mining Via Graph Learning. - Carlos G. Oliver; Dexiong Chen; Vincent Mallet; Pericles Philippopoulos; Karsten M. Borgwardt (CoRR, 2022)

FASM and FAST-YB: Significant Pattern Mining with False Discovery Rate Control. - Paolo Pellizzoni; Karsten M. Borgwardt (ICDM, 2023)

Unsupervised Manifold Alignment with Joint Multidimensional Scaling. - Dexiong Chen; Bowen Fan; Carlos G. Oliver; Karsten M. Borgwardt (ICLR, 2023)

Fisher Information Embedding for Node and Graph Learning. - Dexiong Chen; Paolo Pellizzoni; Karsten M. Borgwardt (ICML, 2023)

ProteinShake: Building datasets and benchmarks for deep learning on protein structures. - Tim Kucera; Carlos G. Oliver; Dexiong Chen; Karsten M. Borgwardt (NeurIPS, 2023)

Higher-order genetic interaction discovery with network-based biological priors. - Paolo Pellizzoni; Giulia Muzio; Karsten M. Borgwardt (Bioinform., 2023)

Weisfeiler and Leman go Machine Learning: The Story so far. - Christopher Morris 001; Yaron Lipman; Haggai Maron; Bastian Rieck; Nils M. Kriege; Martin Grohe; Matthias Fey; Karsten M. Borgwardt (J. Mach. Learn. Res., 2023)

SIMBSIG: similarity search and clustering for biobank-scale data. - Michael F. Adamer; Eljas Roellin; Lucie Bourguignon; Karsten M. Borgwardt (Bioinform., 2023)

Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information. - Giovanni Visonà; Diane Duroux; Lucas Miranda 002; Emese Sükei; Yiran Li; Karsten M. Borgwardt; Carlos G. Oliver (Bioinform., 2023)

On the Expressivity and Sample Complexity of Node-Individualized Graph Neural Networks. - Paolo Pellizzoni; Till Hendrik Schulz; Dexiong Chen; Karsten M. Borgwardt (NeurIPS, 2024)

Structure- and Function-Aware Substitution Matrices via Learnable Graph Matching. - Paolo Pellizzoni; Carlos G. Oliver; Karsten M. Borgwardt (RECOMB, 2024)

Biomarker identification by interpretable maximum mean discrepancy. - Michael F. Adamer; Sarah C. Brüningk; Dexiong Chen; Karsten M. Borgwardt (Bioinform., 2024)

An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit. - Xinrui Lyu; Bowen Fan; Matthias Hüser; Philip Hartout; Thomas Gumbsch; Martin Faltys; Tobias M. Merz; Gunnar Rätsch; Karsten M. Borgwardt (Bioinform., 2024)

Endowing Protein Language Models with Structural Knowledge. - Dexiong Chen; Philip Hartout; Paolo Pellizzoni; Carlos G. Oliver; Karsten M. Borgwardt (CoRR, 2024)

Learning Long Range Dependencies on Graphs via Random Walks. - Dexiong Chen; Till Hendrik Schulz; Karsten M. Borgwardt (ICLR, 2025)

Graph Neural Networks Can (Often) Count Substructures. - Paolo Pellizzoni; Till Hendrik Schulz; Karsten M. Borgwardt (ICLR, 2025)

Detecting Antimicrobial Resistance Through MALDI-TOF Mass Spectrometry with Statistical Guarantees Using Conformal Prediction. - Nina Corvelo Benz; Lucas Miranda 002; Dexiong Chen; Janko Sattler; Karsten M. Borgwardt (RECOMB, 2025)

Flatten Graphs as Sequences: Transformers are Scalable Graph Generators. - Dexiong Chen; Markus Krimmel; Karsten M. Borgwardt (CoRR, 2025)

Towards Fast Graph Generation via Autoregressive Noisy Filtration Modeling. - Markus Krimmel; Jenna Wiens; Karsten M. Borgwardt; Dexiong Chen (CoRR, 2025)

A Comprehensive Benchmark for RNA 3D Structure-Function Modeling. - Luis Wyss; Vincent Mallet; Wissam Karroucha; Karsten M. Borgwardt; Carlos G. Oliver (CoRR, 2025)