G. Rätsch

发表

The SHOGUN Machine Learning Toolbox

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

Large Scale Learning with String Kernels

K. Rieck, S. Sonnenburg, G. Rätsch, 2006 .

mGene: accurate SVM-based gene finding with an application to nematode genomes.

Cheng Soon Ong, S. Sonnenburg, A. Zien, 2009, Genome research.

Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees

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

On Matching Pursuit and Coordinate Descent

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

Linear programs for automatic accuracy control in regression.

B. Schölkopf, Alex Smola, G. Rätsch, 1999 .

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

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

Robust Boosting via Convex Optimization

G. Rätsch, 2001 .

Using support vector machines for time series prediction

Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, 1999 .

Systematic evaluation of spliced alignment programs for RNA-seq data

J. Harrow, R. Guigó, T. Hubbard, 2013, Nature Methods.

An Introduction to Boosting and Leveraging

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

Totally corrective boosting algorithms that maximize the margin

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

On Matching Pursuit and Coordinate Descent

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

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

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

Boosting Algorithms for Maximizing the Soft Margin

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

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

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

Positional Oligomer Importance Matrices

S. Sonnenburg, A. Zien, G. Rätsch, 2007, NIPS 2007.

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

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

Maximizing the Margin with Boosting

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

Support Vector Machines and Kernels for Computational Biology

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

ARTS: accurate recognition of transcription starts in human

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

Large Scale Hidden Semi-Markov SVMs

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

Advanced Lectures on Machine Learning

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

Kernel PCA pattern reconstruction via approximate pre-images.

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

Inferring latent task structure for Multitask Learning by Multiple Kernel Learning

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

Advances in Neural Information Processing Systems 21 (NIPS 2008)

Gunnar Rätsch, Bernhard Schölkopf, Christian Widmer, 2008 .

A New Discriminative Kernel from Probabilistic Models

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

Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning

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

Regularizing AdaBoost

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

Invariant Feature Extraction and Classification in Kernel Spaces

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

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

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

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

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

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

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

pmVAE: Learning Interpretable Single-Cell Representations with Pathway Modules

Stefan G. Stark, G. Rätsch, G. Gut, 2021, bioRxiv.

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.

Multiple insert size paired-end sequencing for deconvolution of complex transcriptomes

G. Rätsch, L. Hartmann, C. Lanz, 2012, RNA biology.

Global effects of the small RNA biogenesis machinery on the Arabidopsis thaliana transcriptome

Stefan R. Henz, G. Zeller, G. Rätsch, 2010, Proceedings of the National Academy of Sciences.

Pan-cancer analysis of whole genomes

Steven J. M. Jones, Nuno A. Fonseca, Icgc, 2020, Nature.

RNA‐Seq Read Alignments with PALMapper

Vipin T. Sreedharan, G. Rätsch, F. D. Bona, 2010, Current protocols in bioinformatics.

Genomewide SNP variation reveals relationships among landraces and modern varieties of rice

Kenneth L. McNally, Richard M. Clark, R. Stokowski, 2009, Proceedings of the National Academy of Sciences.

A spatial and temporal map of C. elegans gene expression.

Vipin T. Sreedharan, Stefan R. Henz, Stephen E. Von Stetina, 2011, Genome research.

Nonsense-Mediated Decay of Alternative Precursor mRNA Splicing Variants Is a Major Determinant of the Arabidopsis Steady State Transcriptome[C][W]

Anil K. Kesarwani, G. Rätsch, Jonas Behr, 2013, Plant Cell.

Registered access: authorizing data access

Alan C. Evans, J. D. Spalding, A. Philippakis, 2018, European Journal of Human Genetics.

Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients

Steven J. M. Jones, Christopher A. Miller, Joshua M. Stuart, 2018, Cancer cell.

Common Sequence Polymorphisms Shaping Genetic Diversity in Arabidopsis thaliana

Richard M. Clark, Tina T. Hu, B. Schölkopf, 2007, Science.

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

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

Assessing the Gene Regulatory Landscape in 1,188 Human Tumors

G. Rätsch, A. Brazma, O. Stegle, 2017, bioRxiv.

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

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

Sequence analysis Dynamic compression schemes for graph coloring

G. Rätsch, A. Kahles, Harun Mustafa, 2018 .

Persistence and Availability of Web Services in Computational Biology

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

Image Construction by Linear Programming

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

Genome-wide analysis of alternative splicing in Volvox carteri

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

Genome-wide analysis of alternative splicing in

Cheng Soon Ong, G. Rätsch, A. Hallmann, 2014 .

Highlights from the Fifth International Society for Computational Biology ( ISCB ) Student Council Symposium

G. Rätsch, O. Kohlbacher, J. Lohmann, 2009 .

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.

Detecting polymorphic regions in Arabidopsis thaliana with resequencing microarrays.

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

LOCAS : A new low coverage assembler for short reads

Shaun D. Jackman, Jenny Q. Qian, S. J. Coleman, 2009 .

nematode genomes mGene : Accurate SVM-based gene finding with an application to Material Supplemental

Cheng Soon Ong, S. Sonnenburg, A. Zien, 2009 .

Accurate prediction of alternative splicing events

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

HnRNP L and L-like cooperate in multiple-exon regulation of CD45 alternative splicing

V. Beneš, G. Rätsch, H. Jäck, 2012, Nucleic acids research.

Prediction of ultra-potent shRNAs with a sequential classification algorithm

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

Robust Ensemble Learning for Data Mining

B. Schölkopf, K. Müller, Alex Smola, 2000, PAKDD.

Discovering Common Sequence Variation in Arabidopsis thaliana

G. Rätsch, A. Bohlen, Schweikert G Clark R, 2008, ISMB 2008.

Pan-cancer study of heterogeneous RNA aberrations

Nuno A. Fonseca, Angela N. Brooks, Stefan G. Stark, 2017, bioRxiv.

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 .

SCIM: universal single-cell matching with unpaired feature sets

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

gene expression C . elegans A spatial and temporal map of Material Supplemental

Vipin T. Sreedharan, Stefan R. Henz, G. Zeller, 2011 .

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.

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

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

Survey of Model Selection Criteria for Large Margin Classifiers

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

Building an international consortium for tracking coronavirus health status

T. Spector, P. Wilmes, Xihong Lin, 2020, Nature Medicine.

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

G. Rätsch, Gideon Dresdner, Maria-Luiza Vladarean, 2022 .

RNA Instant Quality Check: Alignment-Free RNA-Degradation Detection

G. Rätsch, A. Kahles, K. Lehmann, 2022, J. Comput. Biol..

Comparative analysis of mRNA and protein degradation in prostate tissues indicates high stability of proteins

Nora C. Toussaint, Ben C. Collins, G. Rätsch, 2019, Nature Communications.

Mutant SF3B1 promotes PDAC malignancy through TGF-β resistance

G. Rätsch, M. Stoffel, A. Kahles, 2022, bioRxiv.

High-coverage whole-genome analysis of 1220 cancers reveals hundreds of genes deregulated by rearrangement-mediated cis-regulatory alterations

Nuno A. Fonseca, Angela N. Brooks, David T. W. Jones, 2020, Nature Communications.

ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning

Nicholas D. Youngblut, B. Schölkopf, G. Rätsch, 2022, bioRxiv.

PipeIT2: A tumor-only somatic variant calling workflow for Molecular Diagnostic Ion Torrent sequencing data

G. Rätsch, M. Rubin, A. Benjak, 2022, bioRxiv.

Ab-initio gene finding using machine learning

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

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

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

Tumor Necrosis Factor dynamically regulates the mRNA stabilome in rheumatoid arthritis fibroblast-like synoviocytes

Eugenia G. Giannopoulou, G. Rätsch, L. Ivashkiv, 2017, PloS one.

SF3B1 facilitates HIF1-signaling and promotes malignancy in pancreatic cancer.

G. Rätsch, M. Peter, M. Stoffel, 2022, Cell reports.

Genomic Rearrangements Considered as Quantitative Traits

Richard M. Clark, G. Rätsch, R. Mott, 2016, bioRxiv.

Aligning Distant Sequences to Graphs using Long Seed Sketches

G. Rätsch, A. Kahles, Harun Mustafa, 2022, bioRxiv.

mTIM: A margin-based transcript identification method

S. Sonnenburg, G. Rätsch, Jonas Behr, 2011 .

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

G. Rätsch, Christian Widmer, Xinghua Lou, 2014, Signal, Image and Video Processing.

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

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

MetaGraph-MLA: Label-guided alignment to variable-order De Bruijn graphs

G. Rätsch, A. Kahles, Harun Mustafa, 2022, bioRxiv.

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

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

Robust Ensemble Learning

Gunnar Rätsch, Bernhard Schölkopf, Dale Schuurmans, 2000 .

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

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

Robust Boosting via Convex Optimization: Theory and Applications

Gunnar Rätsch, G. Rätsch, 2007 .

c-MYC regulates mRNA translation efficiency and start-site selection in lymphoma

G. Rätsch, A. Viale, Z. Ouyang, 2019, The Journal of experimental medicine.

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.

Robust multi-class boosting

Gunnar Rätsch, G. Rätsch, 2003, INTERSPEECH.

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

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

Learning Interpretable SVMs for Biological Sequence Classification

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

Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces

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

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..

On the Convergence of Leveraging

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

Barrier Boosting

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

A Mathematical Programming Approach to the Kernel Fisher Algorithm

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

Active Learning in the Drug Discovery Process

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

Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites

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

Adapting Codes and Embeddings for Polychotomies

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

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

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

SF3B1 promotes tumor malignancy through splicing-independent co-activation of HIF1α

G. Rätsch, M. Stoffel, H. Moch, 2020, bioRxiv.

Disentangling Factors of Variations Using Few Labels pdf

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

Interferon-γ regulates cellular metabolism and mRNA translation to potentiate macrophage activation

Eugenia G. Giannopoulou, G. Rätsch, C. Rice, 2015, Nature Immunology.

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

Paweł P. Łabaj, Elena M. Vayndorf, Kern Rei Chng, 2019, Nature Medicine.

RNA G-quadruplexes cause eIF4A-dependent oncogene translation in cancer

Konstantinos J. Mavrakis, G. Rätsch, F. Speleman, 2014, Nature.

HBEGF+ macrophages in rheumatoid arthritis induce fibroblast invasiveness

G. Rätsch, S. Raychaudhuri, L. Ivashkiv, 2019, Science Translational Medicine.

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

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

Alternative Splicing Substantially Diversifies the Transcriptome during Early Photomorphogenesis and Correlates with the Energy Availability in Arabidopsis

G. Rätsch, Jonas Behr, L. Hartmann, 2016, Plant Cell.

Genomic basis for RNA alterations in cancer

Mary Goldman, Nuno A. Fonseca, Brian Craft, 2020, Nature.

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

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

RiboDiff: detecting changes of mRNA translation efficiency from ribosome footprints

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

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.

Accurate splice site prediction using support vector machines

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

PALMA: mRNA to genome alignments using large margin algorithms

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

PALMA: Perfect Alignments using Large Margin Algorithms

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

Large scale genomic sequence SVM classifiers

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

Nanotechnology in the Environmental Health Sciences

B. Schölkopf, K. Müller, S. Sonnenburg, 2005, Environmental Health Perspectives.

RASE: recognition of alternatively spliced exons in C.elegans

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

Patterns of somatic structural variation in human cancer genomes

Nuno A. Fonseca, Gary D Bader, Benjamin J. Raphael, 2020, Nature.

HBEGF+ macrophages identified in rheumatoid arthritis promote joint tissue invasiveness and are reshaped differentially by medications

G. Rätsch, S. Raychaudhuri, L. Ivashkiv, 2019, bioRxiv.

The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support.

Nora C. Toussaint, G. Rätsch, H. Moch, 2021, Cancer cell.

MMR: a tool for read multi-mapper resolution

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

Learning single-cell perturbation responses using neural optimal transport

Stefan G. Stark, G. Rätsch, L. Pelkmans, 2021, bioRxiv.

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

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

Species-wide Genetic Incompatibility Analysis Identifies Immune Genes as Hot Spots of Deleterious Epistasis

G. Rätsch, M. Zaidem, D. Weigel, 2014, Cell.

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Nuno A. Fonseca, Gary D Bader, Benjamin J. Raphael, 2020, Nature Communications.

Establishing standardized immune phenotyping of metastatic melanoma by digital pathology

Vipin T. Sreedharan, Nora C. Toussaint, Christian P. Kunze, 2021, Laboratory Investigation.

Polypyrimidine Tract Binding Protein Homologs from Arabidopsis Are Key Regulators of Alternative Splicing with Implications in Fundamental Developmental Processes[W]

G. Rätsch, A. Wachter, A. Kahles, 2012, Plant Cell.

Efficient Margin Maximizing with Boosting

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

Identification of HIF-dependent alternative splicing in gastrointestinal cancers and characterization of a long, coding isoform of SLC35A3.

G. Rätsch, Nicola Zamboni, W. Krek, 2021, Genomics.

Characterisation of HIF-dependent alternative isoforms in pancreatic cancer

G. Rätsch, Nicola Zamboni, W. Krek, 2019, bioRxiv.

RNA‐Seq analysis in mutant zebrafish reveals role of U1C protein in alternative splicing regulation

V. Beneš, G. Rätsch, A. Bindereif, 2011, The EMBO journal.

Global Genetic Cartography of Urban Metagenomes and Anti-Microbial Resistance

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

EIF1AX and RAS Mutations Cooperate to Drive Thyroid Tumorigenesis through ATF4 and c-MYC.

G. Rätsch, J. Blenis, S. Lowe, 2018, Cancer discovery.

SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing

Stefan G. Stark, G. Rätsch, A. Kahles, 2021, bioRxiv.

A global metagenomic map of urban microbiomes and antimicrobial resistance

Stephanie L. Hyland, Nora C. Toussaint, Paweł P. Łabaj, 2021, Cell.

modENCODE Project Genome by the Caenorhabditis elegans Integrative Analysis of the

Raymond K. Auerbach, Kevin Y. Yip, Stefan R. Henz, 2011 .

Targeting eIF4A-Dependent Translation of KRAS Signaling Molecules

Stefan G. Stark, G. Rätsch, A. Viale, 2021, Cancer Research.

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

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

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).

On the Importance of Clinical Notes in Multi-modal Learning for EHR Data

G. Rätsch, Rita Kuznetsova, Hugo Yeche, 2022, ArXiv.

A pan cancer analysis of promoter activity highlights the regulatory role of alternative transcription start sites and their association with noncoding mutations

Nuno A. Fonseca, Angela N. Brooks, G. Rätsch, 2017, bioRxiv.

Improving Clinical Predictions through Unsupervised Time Series Representation Learning pdf

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

Kernel Methods for Predictive Sequence Analysis

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

Butler enables rapid cloud-based analysis of thousands of human genomes

Robert L. Grossman, Oliver Hofmann, Roland Eils, 2020, Nature Biotechnology.

AStarix: Fast and Optimal Sequence-to-Graph Alignment

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

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

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

Cohesin Rings Devoid of Scc3 and Pds5 Maintain Their Stable Association with the DNA

Vipin T. Sreedharan, G. Rätsch, C. Lanz, 2012, PLoS genetics.

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

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

Optimal spliced alignments of short sequence reads

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

Integrated multi-omics reveals anaplerotic rewiring in methylmalonyl-CoA mutase deficiency

Patrick G. A. Pedrioli, C. Howald, G. Rätsch, 2022, Nature Metabolism.

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

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

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

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

A Pan-Cancer Transcriptome Analysis Reveals Pervasive Regulation through Tumor-Associated Alternative Promoters

Nuno A. Fonseca, G. Rätsch, A. Brazma, 2018 .

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

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

Prediction of potent shRNAs with a sequential classification algorithm

Vipin T. Sreedharan, Darjus F. Tschaharganeh, G. Rätsch, 2017, Nature Biotechnology.

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

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

Novel Machine Learning Methods for MHC Class I Binding Prediction

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

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

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

Genomic Rearrangements in Arabidopsis Considered as Quantitative Traits

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

Sparse Binary Relation Representations for Genome Graph Annotation

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

Accurate detection of differential RNA processing

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

Next generation genome annotation with mGene.ngs

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

Transcript quantification with RNA-Seq data

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

Comprehensive molecular characterization of mitochondrial genomes in human cancers

Nuno A. Fonseca, Gary D Bader, Benjamin J. Raphael, 2017, bioRxiv.

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

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

MetaGraph: Indexing and Analysing Nucleotide Archives at Petabase-scale

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

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

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

Author Correction: Pan-cancer analysis of whole genomes

Nuno A. Fonseca, Gary D Bader, Benjamin J. Raphael, 2023, Nature.

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

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

Registered access: authorizing data access

Alan C. Evans, J. D. Spalding, A. Philippakis, 2018, European Journal of Human Genetics.

Sex differences in oncogenic mutational processes

Nuno A. Fonseca, Gary D Bader, Benjamin J. Raphael, 2019, Nature Communications.

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

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

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

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

Multi-task Learning for Computational Biology: Overview and Outlook

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

A General and Efficient Multiple Kernel Learning Algorithm

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

Competitive Training of Mixtures of Independent Deep Generative Models

Gunnar Rätsch, Bernhard Schölkopf, Francesco Locatello, 2018 .

Learning Unitary Operators with Help From u(n)

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

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..

Exploiting physico-chemical properties in string kernels

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

Receptor-type guanylate cyclase is required for carbon dioxide sensation by Caenorhabditis elegans

Stefan R. Henz, H. Horvitz, G. Zeller, 2010, Proceedings of the National Academy of Sciences.

Lossless indexing with counting de Bruijn graphs

G. Rätsch, A. Kahles, Harun Mustafa, 2021, bioRxiv.

Integrated longitudinal analysis of adult grade 4 diffuse gliomas with long-term relapse interval revealed upregulation of TGF-β signaling in recurrent tumors

G. Rätsch, M. Rubin, P. Schucht, 2022, Neuro-oncology.

Active Learning with Support Vector Machines in the Drug Discovery Process

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

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

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

Aligning Distant Sequences to Graphs using Long Seed Sketches

G. Rätsch, A. Kahles, Harun Mustafa, 2022, bioRxiv.

Fast Alignment-Free Similarity Estimation By Tensor Sketching

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

Dynamic compression schemes for graph coloring

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

The Feature Importance Ranking Measure

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

A Brief Introduction into Machine Learning

G. Rätsch, 2004 .

Comparative analysis of mRNA and protein degradation in prostate tissues indicates high stability of proteins

Nora C. Toussaint, Ben C. Collins, G. Rätsch, 2019, Nature Communications.

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

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

Prototype Classification: Insights from Machine Learning

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

Quantitative RNA expression analysis with Affymetrix Tiling 1.0R arrays identifies new E2F target genes.

G. Zeller, G. Rätsch, D. Inzé, 2009, The Plant journal : for cell and molecular biology.

SOM-VAE: Interpretable Discrete Representation Learning on Time Series

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

Predicting circulatory system deterioration in intensive care unit patients

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

Sparse Gaussian Processes on Discrete Domains pdf

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

Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference

G. Rätsch, Alexander Immer, Vincent Fortuin, 2023, ArXiv.

ROS Induction Targets Persister Cancer Cells with Low Metabolic Activity in NRAS-Mutated Melanoma.

Vipin T. Sreedharan, Nora C. Toussaint, Christian P. Kunze, 2023, Cancer research.

Disentangling Factors of Variation Using Few Labels

B. Schölkopf, M. Tschannen, G. Rätsch, 2019, ArXiv.

Probabilistic clustering of time-evolving distance data

Julia E. Vogt, Stefan G. Stark, V. Roth, 2015, Machine-mediated learning.

An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis

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

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

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

Topology-based sparsification of graph annotations

G. Rätsch, A. Kahles, Harun Mustafa, 2020, Bioinform..

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

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

Revealing sequence variation patterns in rice with machine learning methods

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

Transcript Normalization and Segmentation of Tiling Array Data

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

Multivariate Time Series Imputation with Variational Autoencoders pdf

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

The Cancer Genome Atlas Pan-Cancer analysis project

Joshua F. McMichael, James M. Eldred, Martin L. Miller, 2013 .

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

Nuno A. Fonseca, Benjamin J. Raphael, Allison P. Heath, 2020, Nature Communications.

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 .

Boosting Black Box Variational Inference

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

Solving Semi-infinite Linear Programs Using Boosting-Like Methods

Gunnar Rätsch, G. Rätsch, 2006, ALT.

Using Genome Graph Topology to Guide Annotation Matrix Sparsification

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

Cancer Panomics: Computational Methods and Infrastructure for Integrative Analysis of Cancer Highthroughput " Omics " Data

Benjamin J. Raphael, Joshua M. Stuart, Adam A. Margolin, 2022 .

Image reconstruction by linear programming

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

Genome-wide analysis of alternative splicing in Volvox carteri

Cheng Soon Ong, G. Rätsch, A. Hallmann, 2014, BMC Genomics.

A Commentary on the Unsupervised Learning of Disentangled Representations pdf

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

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

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

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

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

Efficient Training of Graph-Regularized Multitask SVMs

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

Machine Learning in Computational Biology

William Stafford Noble, C. Leslie, G. Rätsch, 2007, NIPS 2007.

Boosting Variational Inference: an Optimization Perspective

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

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

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

A Mathematical Programming Approach to the Kernel Fisher Algorithm

K. Müller, S. Mika, G. Rätsch, 2000, NIPS.

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

Stefan R. Henz, G. Zeller, G. Rätsch, 2009, The Plant journal : for cell and molecular biology.

Leveraging Sequence Classification by Taxonomy-Based Multitask Learning

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

An Empirical Analysis of Topic Modeling for Mining Cancer Clinical Notes

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

Improving Neural Additive Models with Bayesian Principles

G. Rätsch, Alexander Immer, Vincent Fortuin, 2023, 2305.16905.

Graph Based Semi-supervised Learning with Sharper Edges

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

A pan cancer analysis of promoter activity highlights the regulatory role of alternative transcription start sites and their association with noncoding mutations

Angela N. Brooks, G. Rätsch, A. Brazma, 2017 .

The SHOGUN Machine Learning Toolbox

S. Sonnenburg, A. Zien, G. Rätsch, 2010, J. Mach. Learn. Res..

Probabilistic clustering of time-evolving distance data

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

DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps

Gunnar Rätsch, Vincent Fortuin, Matthias Hüser, 2019 .

The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support.

Jack Kuipers, Niko Beerenwinkel, Bernd Wollscheid, 2020, Cancer cell.

New Methods for Splice Site Recognition

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

13 Accurate Splice Site Detection for Caenorhabditis elegans

Gunnar Rätsch, Sören Sonnenburg, S. Sonnenburg, 2004 .

Label-guided seed-chain-extend alignment on annotated De Bruijn graphs

G. Rätsch, A. Kahles, Harun Mustafa, 2024, bioRxiv.

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

S. Sonnenburg, A. Zien, G. Rätsch, 2008, ISMB.

Prototype Classification: Insights from Machine Learning

Arnulf B. A. Graf, O. Bousquet, G. Rätsch, 2009, Neural Computation.