Gunnar Rätsch

发表

SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data

Gunnar Rätsch, Yi Zhong, André Kahles, 2016, bioRxiv.

A Mathematical Programming Approach to the Kernel Fisher Algorithm

Gunnar Rätsch, Klaus-Robert Müller, Sebastian Mika, 2000, NIPS.

The Feature Importance Ranking Measure

Gunnar Rätsch, Alexander Zien, Nicole Krämer, 2009, ECML/PKDD.

Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection

Gunnar Rätsch, Manfred K. Warmuth, Koji Tsuda, 2004, J. Mach. Learn. Res..

Boosting Algorithms for Maximizing the Soft Margin

Gunnar Rätsch, Manfred K. Warmuth, Karen A. Glocer, 2007, NIPS.

Kernel Methods for Predictive Sequence Analysis

Gunnar Rätsch, Cheng Soon Ong, G. Rätsch, 2006 .

Software development plan

Gunnar Rätsch, Erika Horn, Alexander Neuhaus, 1996 .

Image reconstruction by linear programming

Gunnar Rätsch, Koji Tsuda, G. Rätsch, 2003, IEEE Transactions on Image Processing.

Efficient Training of Graph-Regularized Multitask SVMs

Gunnar Rätsch, Marius Kloft, Nico Görnitz, 2012, ECML/PKDD.

Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers

Gunnar Rätsch, Klaus-Robert Müller, Sebastian Mika, 2001, ICANN.

An Empirical Analysis of Topic Modeling for Mining Cancer Clinical Notes

Gunnar Rätsch, Stuart Gardos, Katherine Redfield Chang, 2016 .

Revealing sequence variation patterns in rice with machine learning methods

Gunnar Rätsch, David Cox, Georg Zeller, 2008, BMC Bioinformatics.

Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 2000, German Conference on Bioinformatics.

MMR: A tool for Read Multi-Mapper Resolution

Gunnar Rätsch, André Kahles, Jonas Behr, 2015 .

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

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

Oqtans: a Galaxy-integrated workflow for quantitative transcriptome analysis from NGS Data

Gunnar Rätsch, André Kahles, Georg Zeller, 2011, BMC Bioinformatics.

SplAdder: Identification, quantification and testing of alternative splicing events from RNA-Seq data

Gunnar Rätsch, Yi Zhong, André Kahles, 2016 .

The Need for Open Source Software in Machine Learning

Carl E. Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, 2007, J. Mach. Learn. Res..

Efficient graph-color compression with neighborhood-informed Bloom filters

Gunnar Rätsch, André Kahles, Carsten Eickhoff, 2017 .

Learning Unitary Operators with Help From u(n)

Gunnar Rätsch, Stephanie L. Hyland, G. Rätsch, 2016, AAAI.

Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms

Gunnar Rätsch, Klaus-Robert Müller, Marius Kloft, 2015, ECML/PKDD.

Totally corrective boosting algorithms that maximize the margin

Gunnar Rätsch, Manfred K. Warmuth, Jun Liao, 2006, ICML.

An introduction to kernel-based learning algorithms

Gunnar Rätsch, Bernhard Schölkopf, Klaus-Robert Müller, 2001, IEEE Trans. Neural Networks.

MMR: a tool for read multi-mapper resolution

Gunnar Rätsch, André Kahles, Jonas Behr, 2015, bioRxiv.

Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning

Gunnar Rätsch, Bernhard Schölkopf, Klaus-Robert Müller, 2006, PLoS Comput. Biol..

Multivariate Time Series Imputation with Variational Autoencoders pdf

Gunnar Rätsch, Stephan Mandt, Vincent Fortuin, 2019, ArXiv.

MITIE: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples

Gunnar Rätsch, Yi Zhong, André Kahles, 2013, Bioinform..

Integrative Genome-wide Analysis of the Determinants of RNA Splicing in Kidney Renal Clear Cell Carcinoma

Gunnar Rätsch, André Kahles, Oliver Stegle, 2014, bioRxiv.

Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation

Gunnar Rätsch, André Kahles, Sören Sonnenburg, 2011, NIPS.

Exploiting physico-chemical properties in string kernels

Gunnar Rätsch, Oliver Kohlbacher, Christian Widmer, 2010, BMC Bioinformatics.

Efficient privacy-preserving string search and an application in genomics

Gunnar Rätsch, Koji Nuida, Kana Shimizu, 2015, bioRxiv.

Graph-regularized 3D shape reconstruction from highly anisotropic and noisy images

Gunnar Rätsch, Christian Widmer, Philipp Drewe, 2013, Signal, image and video processing.

An asymptotical Analysis and Improvement of AdaBoost in the binary classification case

Gunnar Rätsch, Klaus-Robert Müller, Takashi Onoda, 2000 .

Machine Learning in Bioinformatics

Gunnar Rätsch, Cheng Soon Ong, Alexander Zien, 2009, Encyclopedia of Database Systems.

Machine Learning Algorithms for Polymorphism Detection

Gunnar Rätsch, Gabriele Schweikert, Weigel D Schölkopf B Zeller G, 2006, NIPS 2007.

Optimal spliced alignments of short sequence reads

Gunnar Rätsch, Fabio De Bona, Stephan Ossowski, 2008, BMC Bioinformatics.

Robust Boosting via Convex Optimization: Theory and Applications

Gunnar Rätsch, G. Rätsch, 2007 .

Robust multi-class boosting

Gunnar Rätsch, G. Rätsch, 2003, INTERSPEECH.

v-Arc: Ensemble Learning in the Presence of Outliers

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1999, NIPS.

Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees

Gunnar Rätsch, Martin Jaggi, Francesco Locatello, 2017, NIPS.

Accurate prediction of alternative splicing events

Gunnar Rätsch, Bernhard Schölkopf, Cheng Soon Ong, 2005, NIPS 2005.

Graph Based Semi-supervised Learning with Sharper Edges

Gunnar Rätsch, Hyunjung Shin, N. Jeremy Hill, 2006, ECML.

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

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

Learning Interpretable SVMs for Biological Sequence Classification

Gunnar Rätsch, Sören Sonnenburg, Christin Schäfer, 2005, BMC Bioinformatics.

rQuant.web: a tool for RNA-Seq-based transcript quantitation

Gunnar Rätsch, Regina Bohnert, G. Rätsch, 2010, Nucleic Acids Res..

SCIM: universal single-cell matching with unpaired feature sets

Gunnar Rätsch, Francesco Locatello, Franziska Singer, 2020, bioRxiv.

Invariant Feature Extraction and Classification in Kernel Spaces

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1999, NIPS.

Boosting Black Box Variational Inference

Gunnar Rätsch, Francesco Locatello, Rajiv Khanna, 2018, NeurIPS.

RiboDiff: Detecting Changes of mRNA Translation Efficiency from Ribosome Footprints

Gunnar Rätsch, Yi Zhong, Theofanis Karaletsos, 2015 .

Cancer panomics: Computational methods and nfrastructure for integrative analysis of cancer highthroughput "omics" data

Gunnar Rätsch, Benjamin J. Raphael, Søren Brunak, 2014, PSB 2014.

An Introduction to Boosting and Leveraging

Gunnar Rätsch, Ron Meir, R. Meir, 2002, Machine Learning Summer School.

On Matching Pursuit and Coordinate Descent

Gunnar Rätsch, Bernhard Schölkopf, Martin Jaggi, 2018, ICML 2018.

MetaGraph: Indexing and Analysing Nucleotide Archives at Petabase-scale

Gunnar Rätsch, André Kahles, Mikhail Karasikov, 2020, bioRxiv.

Improving Clinical Predictions through Unsupervised Time Series Representation Learning pdf

Gunnar Rätsch, Stephanie L. Hyland, George Zerveas, 2018, ArXiv.

Framework for Multi-task Multiple Kernel Learning and Applications in Genome Analysis pdf

Gunnar Rätsch, Marius Kloft, Christian Widmer, 2015, ArXiv.

Deep Mean Functions for Meta-Learning in Gaussian Processes pdf

Gunnar Rätsch, Vincent Fortuin, 2019, ArXiv.

Robust Ensemble Learning

Gunnar Rätsch, Bernhard Schölkopf, Dale Schuurmans, 2000 .

Solving Semi-infinite Linear Programs Using Boosting-Like Methods

Gunnar Rätsch, G. Rätsch, 2006, ALT.

The SHOGUN Machine Learning Toolbox

Gunnar Rätsch, Alexander Binder, Alexander Zien, 2010, J. Mach. Learn. Res..

Predicting circulatory system deterioration in intensive care unit patients

Gunnar Rätsch, Stephanie L. Hyland, Martin Faltys, 2018, AIH@IJCAI.

Ab-initio gene finding using machine learning

Gunnar Rätsch, Cheng Soon Ong, Gabriele Schweikert, 2006, NIPS 2006.

Fast Alignment-Free Similarity Estimation By Tensor Sketching

Gunnar Rätsch, André Kahles, Amir Joudaki, 2020, bioRxiv.

PALMA: Perfect Alignments using Large Margin Algorithms

Gunnar Rätsch, Cheng Soon Ong, Bettina Hepp, 2006, German Conference on Bioinformatics.

PALMA: mRNA to genome alignments using large margin algorithms

Gunnar Rätsch, Cheng Soon Ong, Bettina Hepp, 2007, Bioinform..

Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets pdf

Gunnar Rätsch, Andreas Georgiou, Vincent Fortuin, 2019, ArXiv.

Sparse Binary Relation Representations for Genome Graph Annotation

Gunnar Rätsch, André Kahles, Mikhail Karasikov, 2019, RECOMB.

An Empirical Analysis of Topic Modeling for Mining Cancer Clinical Notes

Gunnar Rätsch, Theofanis Karaletsos, Christopher Crosbie, 2013, bioRxiv.

An Improvement of AdaBoost to Avoid Overfitting

Gunnar Rätsch, Klaus-Robert Müller, Takashi Onoda, 1998, ICONIP.

Competitive Training of Mixtures of Independent Deep Generative Models

Gunnar Rätsch, Bernhard Schölkopf, Francesco Locatello, 2018 .

Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 2003, IEEE Trans. Pattern Anal. Mach. Intell..

Large Scale Multiple Kernel Learning

Gunnar Rätsch, Bernhard Schölkopf, Sören Sonnenburg, 2006, J. Mach. Learn. Res..

Transcript quantification with RNA-Seq data

Gunnar Rätsch, Regina Bohnert, Jonas Behr, 2009, BMC Bioinformatics.

Accurate splice site prediction using support vector machines

Gunnar Rätsch, Sören Sonnenburg, Petra Philips, 2007, BMC Bioinformatics.

Advanced lectures on machine learning : ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003 : revised lectures

Gunnar Rätsch, Ulrike von Luxburg, Olivier Bousquet, 2004 .

Inferring latent task structure for Multitask Learning by Multiple Kernel Learning

Gunnar Rätsch, Yasemin Altun, Christian Widmer, 2010, BMC Bioinformatics.

ARTS: accurate recognition of transcription starts in human

Gunnar Rätsch, Alexander Zien, Sören Sonnenburg, 2006, ISMB.

Regularization-Based Multitask Learning With Applications to Genome Biology and Biological Imaging

Gunnar Rätsch, Marius Kloft, Christian Widmer, 2014, KI - Künstliche Intelligenz.

Advanced Lectures on Machine Learning

Gunnar Rätsch, U von Luxburg, Olivier Bousquet, 2004, Lecture Notes in Computer Science.

A Bayesian Nonparametric Approach to Discover Clinico-Genetic Associations across Cancer Types

Gunnar Rätsch, Stephanie L. Hyland, Julia E. Vogt, 2019, bioRxiv.

Input space versus feature space in kernel-based methods

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1999, IEEE Trans. Neural Networks.

When crowds hold privileges: Bayesian unsupervised representation learning with oracle constraints pdf

Gunnar Rätsch, Serge J. Belongie, Theofanis Karaletsos, 2015, ICLR.

RiboDiff: detecting changes of mRNA translation efficiency from ribosome footprints

Gunnar Rätsch, Yi Zhong, David Kuo, 2015, bioRxiv.

Disentangling Factors of Variations Using Few Labels pdf

Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, 2020, ICLR.

Prediction of ultra-potent shRNAs with a sequential classification algorithm

Gunnar Rätsch, Christina S. Leslie, Christian Widmer, 2017, Nature Biotechnology.

A Machine Learning-based Early Warning System for Circulatory System Deterioration in Intensive Care Unit Patients

Gunnar Rätsch, Stephanie L. Hyland, Martin Faltys, 2018, AMIA.

KIRMES: kernel-based identification of regulatory modules in euchromatic sequences

Gunnar Rätsch, Oliver Kohlbacher, Wolfgang Busch, 2009, BMC Bioinformatics.

Support Vector Machines and Kernels for Computational Biology

Gunnar Rätsch, Bernhard Schölkopf, Cheng Soon Ong, 2008, PLoS Comput. Biol..

Oqtans: the RNA-seq workbench in the cloud for complete and reproducible quantitative transcriptome analysis

Gunnar Rätsch, André Kahles, Georg Zeller, 2014, Bioinform..

Sparse Binary Relation Representations for Genome Graph Annotation

Gunnar Rätsch, André Kahles, Mikhail Karasikov, 2019, Journal of computational biology : a journal of computational molecular cell biology.

Multitask Learning in Computational Biology

Gunnar Rätsch, Christian Widmer, 2012, ICML Unsupervised and Transfer Learning.

SOM-VAE: Interpretable Discrete Representation Learning on Time Series pdf

Gunnar Rätsch, Francesco Locatello, Vincent Fortuin, 2019, ICLR.

Using Genome Graph Topology to Guide Annotation Matrix Sparsification

Gunnar Rätsch, André Kahles, Mikhail Karasikov, 2020, bioRxiv.

Mutational signature learning with supervised negative binomial non-negative matrix factorization

Gunnar Rätsch, Kjong-Van Lehmann, Xinrui Lyu, 2020, Bioinform..

SHOGUN - A Large Scale Machine Learning Toolbox

Gunnar Rätsch, Fabio De Bona, 2006 .

Robust Ensemble Learning for Data Mining

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 2000, PAKDD.

Large Scale Hidden Semi-Markov SVMs

Gunnar Rätsch, Sören Sonnenburg, S. Sonnenburg, 2006, NIPS.

Optimal spliced alignments of short sequence reads

Gunnar Rätsch, Fabio De Bona, Stephan Ossowski, 2008, ECCB.

Dynamic compression schemes for graph coloring

Gunnar Rätsch, André Kahles, Carsten Eickhoff, 2018 .

AStarix: Fast and Optimal Sequence-to-Graph Alignment

Gunnar Rätsch, André Kahles, Martin T. Vechev, 2020, RECOMB.

An asymptotic analysis of AdaBoost in the binary classification case

Gunnar Rätsch, Klaus-Robert Müller, Takashi Onoda, 1998 .

A New Discriminative Kernel from Probabilistic Models

Gunnar Rätsch, Motoaki Kawanabe, Klaus-Robert Müller, 2001, Neural Computation.

Survey of Model Selection Criteria for Large Margin Classifiers

Gunnar Rätsch, Takashi Onoda, Klaus-R. Muller, 2002 .

Novel Machine Learning Methods for MHC Class I Binding Prediction

Gunnar Rätsch, Yasemin Altun, Oliver Kohlbacher, 2010, PRIB.

AStarix: Fast and Optimal Sequence-to-Graph Alignment

Gunnar Rätsch, André Kahles, Martin Vechev, 2020, bioRxiv.

Soft Margins for AdaBoost

Gunnar Rätsch, Klaus-Robert Müller, Takashi Onoda, 2001, Machine Learning.

Protein translational control and its contribution to oncogenesis revealed by computational methods

Gunnar Rätsch, Yi Zhong, Philipp Drewe, 2015, BMC Bioinformatics.

Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps pdf

Gunnar Rätsch, Vincent Fortuin, Matthias Hüser, 2019, ArXiv.

Support Vector Machines for Active Learning in the Drug Discovery Process

Gunnar Rätsch, Manfred K. Warmuth, Christian Lemmen, 2002 .

A Commentary on the Unsupervised Learning of Disentangled Representations pdf

Gunnar Rätsch, Bernhard Schölkopf, Francesco Locatello, 2020, AAAI.

Image Construction by Linear Programming

Gunnar Rätsch, Bernhard Schölkopf, Lawrence K. Saul, 2004, NIPS 2004.

Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces

Gunnar Rätsch, Ayhan Demiriz, Kristin P. Bennett, 2002, Machine Learning.

Classifying 'Drug-likeness' with Kernel-Based Learning Methods

Gunnar Rätsch, Klaus-Robert Müller, Sebastian Mika, 2005, J. Chem. Inf. Model..

Robustes Boosting durch konvexe Optimierung

Gunnar Rätsch, 2001, Ausgezeichnete Informatikdissertationen.

mGene.web: a web service for accurate computational gene finding

Gunnar Rätsch, Cheng Soon Ong, Alexander Zien, 2009, Nucleic Acids Res..

mGene: A Novel Discriminative Gene Finder

Gunnar Rätsch, Zien A Zeller G, Gabriele Schweikert, 2008 .

An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis

Gunnar Rätsch, Bernhard Schölkopf, Christian Widmer, 2008, NIPS.

Machine Learning with Interdependent and Non-identically Distributed Data (Dagstuhl Seminar 15152)

Gunnar Rätsch, Trevor Darrell, Marius Kloft, 2015, Dagstuhl Reports.

Advances in Neural Information Processing Systems 21 (NIPS 2008)

Gunnar Rätsch, Bernhard Schölkopf, Christian Widmer, 2008 .

KIRMES: kernel-based identification of regulatory modules in euchromatic sequences

Gunnar Rätsch, Oliver Kohlbacher, Wolfgang Busch, 2009, Bioinformatics.

SOM-VAE: Interpretable Discrete Representation Learning on Time Series

Gunnar Rätsch, Francesco Locatello, Matthias Hüser, 2018, ICLR 2018.

Kernel PCA and De-Noising in Feature Spaces

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1998, NIPS.

Reconstructing tumor evolutionary histories and clone trees in polynomial-time with SubMARine

Gunnar Rätsch, Jens Stoye, Quaid Morris, 2020, bioRxiv.

Clustering Meets Implicit Generative Models pdf

Gunnar Rätsch, Bernhard Schölkopf, Francesco Locatello, 2018, ICLR.

Knowledge Transfer with Medical Language Embeddings pdf

Gunnar Rätsch, Stephanie L. Hyland, Theofanis Karaletsos, 2016, ArXiv.

RASE: recognition of alternatively spliced exons in C.elegans

Gunnar Rätsch, Bernhard Schölkopf, Sören Sonnenburg, 2005, ISMB.

Ecological Modeling from Time-Series Inference: Insight into Dynamics and Stability of Intestinal Microbiota

Gunnar Rätsch, Chris Sander, Vanni Bucci, 2013, PLoS Comput. Biol..

Efficient Margin Maximizing with Boosting

Gunnar Rätsch, Manfred K. Warmuth, G. Rätsch, 2005, J. Mach. Learn. Res..

A Generative Model of Words and Relationships from Multiple Sources

Gunnar Rätsch, Stephanie L. Hyland, Theofanis Karaletsos, 2016, AAAI.

Boosting Variational Inference: an Optimization Perspective

Gunnar Rätsch, Joydeep Ghosh, Francesco Locatello, 2017, AISTATS.

Machine Learning in Computational Biology

Gunnar Rätsch, William Stafford Noble, Gal Chechik, 2007, NIPS 2007.

Leveraging Sequence Classification by Taxonomy-Based Multitask Learning

Gunnar Rätsch, Yasemin Altun, Christian Widmer, 2010, RECOMB.

New Methods for Splice Site Recognition

Gunnar Rätsch, Klaus-Robert Müller, Sören Sonnenburg, 2002, ICANN.

Using support vector machines for time series prediction

Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, 1999 .

POIMs: positional oligomer importance matrices—understanding support vector machine-based signal detectors

Gunnar Rätsch, Alexander Zien, Sören Sonnenburg, 2008, ISMB.

Next generation genome annotation with mGene.ngs

Gunnar Rätsch, Georg Zeller, Regina Bohnert, 2010, BMC Bioinformatics.

Multi-task Learning for Computational Biology: Overview and Outlook

Gunnar Rätsch, Marius Kloft, Christian Widmer, 2013, Empirical Inference.

Barrier Boosting

Gunnar Rätsch, Klaus-Robert Müller, Sebastian Mika, 2000, COLT.

A General and Efficient Multiple Kernel Learning Algorithm

Gunnar Rätsch, Sören Sonnenburg, Christin Schäfer, 2005, NIPS.

Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies pdf

Gunnar Rätsch, Fernando Pérez-Cruz, Stephanie L. Hyland, 2019, ArXiv.

On the Convergence of Leveraging

Gunnar Rätsch, Sebastian Mika, Manfred K. Warmuth, 2001, NIPS.

Support vector machines-based identification of alternative splicing in Arabidopsis thaliana from whole-genome tiling arrays

Gunnar Rätsch, Johannes Eichner, Georg Zeller, 2011, BMC Bioinformatics.

Sparse Gaussian Processes on Discrete Domains pdf

Gunnar Rätsch, Vincent Fortuin, Heiko Strathmann, 2018, IEEE Access.

Adapting Codes and Embeddings for Polychotomies

Gunnar Rätsch, Alexander J. Smola, Sebastian Mika, 2002, NIPS.

Regularizing AdaBoost

Gunnar Rätsch, Klaus-Robert Müller, Takashi Onoda, 1998, NIPS.

Predicting Time Series with Support Vector Machines

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1997, ICANN.

On Matching Pursuit and Coordinate Descent

Gunnar Rätsch, Bernhard Schölkopf, Martin Jaggi, 2018, ICML.

Probabilistic clustering of time-evolving distance data

Gunnar Rätsch, Volker Roth, Marius Kloft, 2015, Machine Learning.

Dynamic compression schemes for graph coloring

Gunnar Rätsch, André Kahles, Carsten Eickhoff, 2017, bioRxiv.

Oqtans: a multifunctional workbench for RNA-seq data analysis

Gunnar Rätsch, André Kahles, Georg Zeller, 2014, BMC Bioinformatics.

Learning Interpretable SVMs for Biological Sequence Classification

Gunnar Rätsch, Sören Sonnenburg, Christin Schäfer, 2006, BMC Bioinformatics.

Active Learning with Support Vector Machines in the Drug Discovery Process

Gunnar Rätsch, Manfred K. Warmuth, Christian Lemmen, 2003, J. Chem. Inf. Comput. Sci..

Prototype Classification: Insights from Machine Learning

Gunnar Rätsch, Bernhard Schölkopf, Olivier Bousquet, 2009, Neural Computation.

Sparse Binary Relation Representations for Genome Graph Annotation

Gunnar Rätsch, André Kahles, Mikhail Karasikov, 2018, bioRxiv.

An Arcing algorithm with an intuitive learning control parameter

Gunnar Rätsch, Takashi Onoda, Klaus Muller, 2001 .

Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification

Gunnar Rätsch, Bernhard Schölkopf, Klaus-Robert Müller, 2002, IEEE Trans. Pattern Anal. Mach. Intell..

The Solution of Semi-Infinite Linear Programs Using Boosting-Like Methods

Gunnar Rätsch, 2006, Discovery Science.

NIPS workshop on New Problems and Methods in Computational Biology

Gunnar Rätsch, William Stafford Noble, Gal Chechik, 2007, BMC Bioinformatics.

Maximizing the Margin with Boosting

Gunnar Rätsch, Manfred K. Warmuth, G. Rätsch, 2002, COLT.

Active Learning in the Drug Discovery Process

Gunnar Rätsch, Manfred K. Warmuth, Christian Lemmen, 2001, NIPS.

Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs pdf

Gunnar Rätsch, Stephanie L. Hyland, Cristóbal Esteban, 2017, ArXiv.

Applying Advanced Data Analysis and Modeling Techniques to Biological Data

Gunnar Rätsch, 2016 .

CANCER PANOMICS: COMPUTATIONAL METHODS AND INFRASTRUCTURE FOR INTEGRATIVE ANALYSIS OF CANCER HIGH-THROUGHPUT “OMICS” DATA- SESSION INTRODUCTION

Gunnar Rätsch, Søren Brunak, Joshua M. Stuart, 2013 .

Efficient Privacy-Preserving String Search and an Application in Genomics

Gunnar Rätsch, Koji Nuida, Kana Shimizu, 2015 .

Communication-Efficient Jaccard similarity for High-Performance Distributed Genome Comparisons

Gunnar Rätsch, Torsten Hoefler, Edgar Solomonik, 2019, 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

Large scale genomic sequence SVM classifiers

Gunnar Rätsch, Bernhard Schölkopf, Sören Sonnenburg, 2005, ICML.

Transcript Normalization and Segmentation of Tiling Array Data

Gunnar Rätsch, Georg Zeller, Detlef Weigel, 2007, Pacific Symposium on Biocomputing.

Kernel PCA pattern reconstruction via approximate pre-images.

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1998 .

T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states

Gunnar Rätsch, Martin Faltys, Julia E. Vogt, 2021, CHIL.

Reconstructing tumor evolutionary histories and clone trees in polynomial-time with SubMARine

Gunnar Rätsch, Jens Stoye, Quaid Morris, 2021, PLoS Comput. Biol..

Topology-based sparsification of graph annotations

Gunnar Rätsch, André Kahles, Mikhail Karasikov, 2021, Bioinform..

HiRID-ICU-Benchmark - A Comprehensive Machine Learning Benchmark on High-resolution ICU Data pdf

Gunnar Rätsch, Martin Faltys, Xinrui Lyu, 2021, NeurIPS Datasets and Benchmarks.

Meta Learning : Learning to Predict the Leave – one – out Error

Gunnar Rätsch, Klaus-Robert Müller, Sebastian Mika, 2007 .

Genomic Rearrangements in Arabidopsis Considered as Quantitative Traits

Gunnar Rätsch, André Kahles, Jotun Hein, 2017, Genetics.

DNA methylation in Arabidopsis has a genetic basis and shows evidence of local adaptation

Gunnar Rätsch, André Kahles, Philipp Drewe, 2015, eLife.

Persistence and Availability of Web Services in Computational Biology

Gunnar Rätsch, G. Rätsch, Sebastian J. Schultheiss, 2011, PloS one.

A Pan-cancer Transcriptome Analysis Reveals Pervasive Regulation through Alternative Promoters

Gunnar Rätsch, Nuno A. Fonseca, André Kahles, 2019, Cell.

DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps

Gunnar Rätsch, Vincent Fortuin, Matthias Hüser, 2019 .

Accurate detection of differential RNA processing

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

Mar ginal Boosting 1

Gunnar Rätsch, Manfred K. Warmuth, 2001 .

RNA secondary structure prediction using large margin methods

Gunnar Rätsch, Cheng Soon Ong, Alexander Zien, 2007, ISMB 2007.

SVM2Motif—Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor

Gunnar Rätsch, Klaus-Robert Müller, Marius Kloft, 2015, PloS one.

Stress-induced changes in the Arabidopsis thaliana transcriptome analyzed using whole-genome tiling arrays.

Gunnar Rätsch, Timo Sachsenberg, Detlef Weigel, 2009, The Plant journal : for cell and molecular biology.

Global Genetic Cartography of Urban Metagenomes and Anti-Microbial Resistance

Gunnar Rätsch, Emmanuel Dias-Neto, Niranjan Nagarajan, 2019, bioRxiv.

13 Accurate Splice Site Detection for Caenorhabditis elegans

Gunnar Rätsch, Sören Sonnenburg, 2004 .

Genomic basis for RNA alterations revealed by whole-genome analyses of 27 cancer types

Gunnar Rätsch, Junjun Zhang, Nuno A. Fonseca, 2017 .

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

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

Germline determinants of the somatic mutation landscape in 2,642 cancer genomes

Gunnar Rätsch, Mark Gerstein, Li Ding, 2017, bioRxiv.

BRCA Challenge: BRCA Exchange as a global resource for variants in BRCA1 and BRCA2

Gunnar Rätsch, Brian Craft, David Haussler, 2018, PLoS genetics.

Dual roles of the nuclear cap-binding complex and SERRATE in pre-mRNA splicing and microRNA processing in Arabidopsis thaliana

Gunnar Rätsch, Wolfgang Busch, Timo Sachsenberg, 2008, Proceedings of the National Academy of Sciences.

Multi-task Multiple Kernel Learning

Gunnar Rätsch, Marius Kloft, Christian Widmer, 2013 .

Detecting polymorphic regions in Arabidopsis thaliana with resequencing microarrays.

Gunnar Rätsch, Detlef Weigel, Korbinian Schneeberger, 2008, Genome research.

Genome-wide analysis of alternative splicing in Volvox carteri

Gunnar Rätsch, Cheng Soon Ong, Armin Hallmann, 2014, BMC Genomics.

Cartography of opportunistic pathogens and antibiotic resistance genes in a tertiary hospital environment

Gunnar Rätsch, Emmanuel Dias-Neto, Niranjan Nagarajan, 2019, bioRxiv.

At-TAX: a whole genome tiling array resource for developmental expression analysis and transcript identification in Arabidopsis thaliana

Gunnar Rätsch, Bernhard Schölkopf, Timo Sachsenberg, 2008, Genome Biology.