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Mag. Dr. Günter Klambauer

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Forschungsthemen

  • Deep Learning
  • Self-normalizing neural networks
  • Convolutional neural networks
  • Recurrent neural networks and reinforcement learning
  • Machine learning methods for drug discovery
  • Life science data analysis and bioinformatics

Curriculum Vitae

Ausgewählte Publikationen

  • Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter (2017). Self-Normalizing Neural Networks. Advances in Neural Information Processing Systems 30, 972--981. [PDF]
  • Kristina Preuer, Richard P Lewis, Sepp Hochreiter, Andreas Bender, Krishna C Bulusu, and Günter Klambauer (2017). DeepSynergy: Predicting anti-cancer drug synergy with Deep Learning. Bioinformatics, Advance Access. doi: 10.1093/bioinformatics/btx806
  • Jaak Simm, Günter Klambauer, Adam Arany, ... & Hugo Ceulemans (2017). Repurposed high-throughput images enable biological activity prediction for drug discovery. bioRxiv, 108399.doi: 10.1101/108399
  • Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Sepp Hochreiter (2016). DeepTox: Toxicity Prediction using Deep Learning, Frontiers in Environmental Science, 3:80. doi: 10.3389/fenvs.2015.00080
  • Federica Eduati, Lara M Mangravite, Tao Wang, Hao Tang, ... , Sepp Hochreiter, Günter Klambauer, Andreas Mayr, ... , Ivan Rusyn, Fred A Wright, Gustavo Stolovitzky, Yang Xie, and Julio Saez-Rodriguez (2015). Prediction of human population responses to toxic compounds by a collaborative competition. Nature Biotechnology, advance online publication. http://doi:10.1038/nbt.3299.
  • Günter Klambauer, Martin Wischenbart, Michael Mahr, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter (2015). Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map. Bioinformatics, advance access. http://doi:10.1093/bioinformatics/btv373.
  • Günter Klambauer, Bie Verbist, Liesbet Vervoort, Willem Talloen, QSTAR Consortium, Ziv Shkedy, Olivier Thas, Andreas Bender, Hinrich W.H. Göhlmann, Sepp Hochreiter (2015). Using transcriptomics to guide lead optimization in drug discovery projects. Drug Discovery Today, 20(5). http://dx.doi.org/10.1016/j.drudis.2014.12.014.
  • Thomas Unterthiner, Andreas Mayr, Günter Klambauer, Marvin Steijaert, Jörg Wegner, Hugo Ceulemans, Sepp Hochreiter (2014). Deep Learning as an Opportunity in Virtual Screening,Deep Learning and Representation Learning Workshop, in conjunction with Neural Information Processing Systems (NIPS 2014), Montreal, Canada, 2014, PDF
  • Klambauer, G., Unterthiner, T., & Hochreiter, S. (2013). DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions. Nucleic Acids Research, 41(21), e198-e198. doi:10.1093/nar/gkt834
  • Klambauer, G., Schwarzbauer, K., Mayr, A., Clevert, D. A., Mitterecker, A., Bodenhofer, U., & Hochreiter, S. (2012). cn. MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate. Nucleic Acids Research, 40(9), e69-e69. doi:10.1093/nar/gks003

Wissenschaftliche Herausforderungen

Betreute Masterarbeiten

  • Backward dynamics of self-normalizing networks, Pieter-Jan Hoedt, PDF
  • Generative RNN models for molecular strings with biological activity profiles, ongoing
  • Generative adversarial networks for molecular graphs, ongoing
  • Improving image-based compound activity prediction with convolutional neural networks, ongoing
  • Deep Learning for Drug Combination Synergy Prediction
  • The Maximum Common Subgraph Kernel For Predicting Kinase Inhibitors
  • Panelcn.MOPS reaches clinical standards as a copy number variation detection tool for targeted panel sequencing
  • Accurate detection of tumor copy number variations in high-throughput sequencing data
  • Detecting CNVs in the 1000 Genomes Project Data Using cn.MOPS and Relating the Results to Transcriptome Sequencing Data

Ausbildung und Persönliche Daten

  • Since 2014: Post-Doc Researcher at Johannes Kepler University Linz
  • 2014: Award of Excellence of the Austrian Ministry of Science
  • 04/2014: PhD in Bioinformatics (JKU Linz)
  • 2012: Austrian Life Science Award
  • 2009-2014: Researcher at Johannes Kepler University Linz
  • 2007-2009: Secondary School Teacher, BORG Linz and BORG Bad Leonfelden
  • 07/2007: MSc in Mathematics and Biology (University of Vienna)
  • 09/2006-02/2007: Studies Abroad, Universita degli Studi, Padova
  • 2001-2007: Mathematics and Biology Diploma-Studies, University of Vienna

Lehre

  • 2017: Special Topics on Bioinformatics: Population genetics, Lecturer, Johannes Kepler University, Linz, Austria.
  • 2016: Special Topics on Bioinformatics: Population genetics, Lecturer, Johannes Kepler University, Linz, Austria.
  • 2014: Special Topics on Bioinformatics: Population genetics, Lecturer, Johannes Kepler University, Linz, Austria.
  • 2013: Special Topics on Bioinformatics: Population genetics, Lecturer, Johannes Kepler University, Linz, Austria.
  • 2012/2013: Sequence Analysis and Phylogenetics, Lecturer, Johannes Kepler University, Linz, Austria.
  • 2012: Special Topics on Bioinformatics: Population genetics, Lecturer, Johannes Kepler University, Linz, Austria.
  • 2011/2012: Exercises in Bioinformatics I: Sequence Analysis and Phylogenetics, Lecturer, Johannes Kepler University, Linz, Austria.
  • 2011: Special Topics on Bioinformatics: Population genetics, Lecturer, Johannes Kepler University, Linz, Austria.
  • 2010/2011: Exercises in Bioinformatics I: Sequence Analysis and Phylogenetics, Lecturer, Johannes Kepler University, Linz, Austria.
  • 2010: Special Topics on Bioinformatics: Population genetics, Lecturer, Johannes Kepler University, Linz, Austria.
  • 2009/2010: Exercises in Bioinformatics I: Sequence Analysis and Phylogenetics, Lecturer, Johannes Kepler University, Linz, Austria.
  • 2008-2009: Mathematics and Biology, Secondary School Teacher, BORG Linz, Linz
  • 2007-2008: Mathematics and Biology, Secondary School Teacher, BORG Bad Leonfelden, Bad Leonfelden