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Publikationen 2017

Zeitschriften

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning Bioinformatics Learning the High-Dimensional Immunogenomic Features That Predict Public and Private Antibody Repertoires J Immunol Rectified factor networks for biclustering of omics data Bioinformatics panelcn.MOPS: Copy number detection in targeted NGS panel data for clinical diagnostics Human Mutation

Konferenzen

Self-Normalizing Neural Networks NIPS
Author(s): Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
Published: Conference Neural Information Processing Systems Foundation (NIPS 2017), December 4-9, 2017
GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium NIPS
Author(s): Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Günter Klambauer, Sepp Hochreiter
Published: Conference Neural Information Processing Systems Foundation (NIPS 2017), December 4-9, 2017
Robust Learning by Conditional Independence of Hidden Units NIPS
Author(s): Karin Schwarzbauer, Markus Hofmarcher, Thomas Adler, Sepp Hochreiter
Published: Women in Machine Learning Workshop, Conference Neural Information Processing Systems Foundation (NIPS 2017), December 4-9, 2017
DeepSynergy: Prediction of synergies of anti-cancer drugs with Deep Learning NIPS
Author(s): Kristina Preuer, Richard P.I. Lewis, Sepp Hochreiter, Andreas Bender, Krishna C. Bulusu, Günter Klambauer
Published: Women in Machine Learning Workshop, Conference Neural Information Processing Systems Foundation (NIPS 2017), December 4-9, 2017
Convolutional Neural Networks for Subcellular Protein Localization NIPS
Author(s): Elisabeth Rumetshofer, Markus Hofmarcher, Sepp Hochreiter, Günter Klambauer
Published: Women in Machine Learning Workshop, Conference Neural Information Processing Systems Foundation (NIPS 2017), December 4-9, 2017
panelcn.MOPS - a mixture of Poissons model for copy number detection NIPS
Author(s): Gundula Povysil, Günter Klambauer, Katharina Wimmer, Sepp Hochreiter
Published: Women in Machine Learning Workshop, Conference Neural Information Processing Systems Foundation (NIPS 2017), December 4-9, 2017
HapRFN: Rectified Factor Networks for identifying short IBD segments NIPS
Author(s): Gundula Povysil, Djork-Arné Clevert, Sepp Hochreiter
Published: Workshop on Machine Learning in Computational Biology, Conference Neural Information Processing Systems Foundation (NIPS 2017), December 4-9, 2017
Position kernels as a key to making sense of very rare and private single-nucleotide variants NIPS
Author(s): Ulrich Bodenhofer, Sepp Hochreiter
Published: Workshop on Machine Learning in Computational Biology, Conference Neural Information Processing Systems Foundation (NIPS 2017), December 4-9, 2017
Download: PDF, öffnet eine externe URL in einem neuen Fenster
Machine Learning-Based Risk Profile Classification: A Case Study for Heart Valve Surgery NIPS
Author(s): Ulrich Bodenhofer, Bettina Haslinger-Eisterer, Alexander Minichmayer, Georg Hermanutz, Jens Meier
Published: Workshop on Machine Learning for Health, Conference Neural Information Processing Systems Foundation (NIPS 2017), December 4-9, 2017
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Deep Learning for Prediction of Synergistic Effects of Anti-cancer Drugs NIPS
Author(s): Kristina Preuer, Richard P.I. Lewis, Sepp Hochreiter, Andreas Bender, Krishna C. Bulusu, Günter Klambauer
Published: Workshop on Machine Learning for Molecules and Materials, Conference Neural Information Processing Systems Foundation (NIPS 2017), December 4-9, 2017
Deep Learning Yields Virtual Assays NIPS
Author(s): Thomas Unterthiner, Günter Klambauer, Andreas Mayr, Sepp Hochreiter
Published: Workshop on Machine Learning for Molecules and Materials, Conference Neural Information Processing Systems Foundation (NIPS 2017), December 4-9, 2017
Toxicity Prediction Using Self-normalizing Networks NIPS
Author(s): Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
Published: Workshop on Machine Learning for Molecules and Materials, Conference Neural Information Processing Systems Foundation (NIPS 2017), December 4-9, 2017

Bücher/Buch Kapitel

Machine Learning-Based Individualized Prognosis Whitebook Medical Technology
Author(s): Ulrich Bodenhofer, Jens Meier, Bettina Haslinger-Eisterer
Published: Whitebook Medical Technology, ISBN 978-3-200-05215-4, pp. 22-23, 2017

Publikationen (non-peer-reviewed)

GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium arXiv.org Self-Normalizing Neural Networks arXiv.org Repurposed High-Throughput Images Enable Biological Activity Prediction For Drug Discovery biorxiv.org
Author(s): Jaak Simm, Guenter Klambauer, Adam Arany, Marvin Steijaert, Joerg Kurt Wegner, Emmanuel Gustin, Vladimir Chupakhin, Yolanda T. Chong, Jorge Vialard, Peter Buijnsters, Ingrid Velter, Alexander Vapirev, Shantanu Singh, Anne Carpenter, Roel Wuyts, Sepp Hochreiter, Yves Moreau, Hugo Ceulemans
Published: biorxiv.org, March 2017
Download: doi:10.1101/108399, öffnet eine externe URL in einem neuen Fenster

Abschlussarbeiten

Moment Dynamics in Self-Normalising Neural Networks Master Thesis
Published: Master Thesis, 2017
Author(s): Pieter-Jan Hoedt
Analyzing NGS Data with Machine Learning - From IBD Segments to Copy Number Variations PhD Thesis
Published: PhD Thesis, 2017
Author(s): Gundula Povysil
Comparison of Rare Variant Association Tests on UK10K Whole Genome Sequencing Data Master Thesis
Published: Master Thesis, 2017
Author(s): Bellatrix Szantho
Convolutional LSTM for Next Frame Prediction Master Thesis
Published: Master Thesis, 2017
Author(s): Thomas Adler
About gradient based importance weighting in feed-forward artificial neural networks Master Thesis 
Published: Master Thesis, 2017
Author(s): Hubert Ramsauer