| Self-Normalizing Neural Networks G Klambauer, T Unterthiner, A Mayr, S Hochreiter Advances in Neural Information Processing Systems 30, 972--981, 2017 | 906 | 2017 |
| DeepTox: toxicity prediction using deep learning A Mayr, G Klambauer, T Unterthiner, S Hochreiter Frontiers in Environmental Science 3, 80, 2016 | 308 | 2016 |
| cn. MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate G Klambauer, K Schwarzbauer, A Mayr, DA Clevert, A Mitterecker, ... Nucleic acids research 40 (9), e69, 2012 | 296 | 2012 |
| Deep learning as an opportunity in virtual screening T Unterthiner, A Mayr, G Klambauer, M Steijaert, JK Wegner, ... Deep Learning and Representation Learning Workshop, NIPS 2014, 2014 | 128 | 2014 |
| Large-scale comparison of machine learning methods for drug target prediction on ChEMBL A Mayr, G Klambauer, T Unterthiner, M Steijaert, JK Wegner, ... Chemical science 9 (24), 5441-5451, 2018 | 113 | 2018 |
| Prediction of human population responses to toxic compounds by a collaborative competition F Eduati, LM Mangravite, T Wang, H Tang, S Hochreiter, G Klambauer, ... Nature biotechnology, 2015 | 84 | 2015 |
| How adverse outcome pathways can aid the development and use of computational prediction models for regulatory toxicology C Wittwehr, H Aladjov, G Ankley, HJ Byrne, J de Knecht, E Heinzle, ... Toxicological Sciences 155 (2), 326-336, 2017 | 80 | 2017 |
| DeepSynergy: predicting anti-cancer drug synergy with Deep Learning K Preuer, RPI Lewis, S Hochreiter, A Bender, KC Bulusu, G Klambauer Bioinformatics 34 (9), 1538-1546, 2018 | 79 | 2018 |
| Repurposed high-throughput image assays enables biological activity prediction for drug discovery J Simm, G Klambauer, A Arany, M Steijaert, JK Wegner, E Gustin, ... Cell Chemical Biology, 108399, 2018 | 68 | 2018 |
| Toxicity prediction using deep learning T Unterthiner, A Mayr, G Klambauer, S Hochreiter arXiv preprint arXiv:1503.01445, 2015 | 66 | 2015 |
| Using transcriptomics to guide lead optimization in drug discovery projects G Klambauer, B Verbist, L Vervoort, W Talloen, TheQSTARConsortium, ... Drug Discovery Today 20 (5), 505–513, 2015 | 55* | 2015 |
| Fréchet ChemNet distance: a metric for generative models for molecules in drug discovery K Preuer, P Renz, T Unterthiner, S Hochreiter, G Klambauer Journal of chemical information and modeling 58 (9), 1736-1741, 2018 | 45 | 2018 |
| Fréchet ChemNet distance: a metric for generative models for molecules in drug discovery K Preuer, P Renz, T Unterthiner, S Hochreiter, G Klambauer Journal of chemical information and modeling 58 (9), 1736-1741, 2018 | 45 | 2018 |
| Coulomb GANs: Provably optimal nash equilibria via potential fields T Unterthiner, B Nessler, C Seward, G Klambauer, M Heusel, ... arXiv preprint arXiv:1708.08819, 2017 | 43 | 2017 |
| Accurate prediction of biological assays with high-throughput microscopy images and convolutional networks M Hofmarcher, E Rumetshofer, DA Clevert, S Hochreiter, G Klambauer Journal of chemical information and modeling 59 (3), 1163-1171, 2019 | 29* | 2019 |
| panelcn. MOPS: Copy‐number detection in targeted NGS panel data for clinical diagnostics G Povysil, A Tzika, J Vogt, V Haunschmid, L Messiaen, J Zschocke, ... Human mutation 38 (7), 889-897, 2017 | 26 | 2017 |
| DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions G Klambauer, T Unterthiner, S Hochreiter Nucleic acids research 41 (21), e198, 2013 | 26 | 2013 |
| cn. FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate DA Clevert, A Mitterecker, A Mayr, G Klambauer, M Tuefferd, AD Bondt, ... Nucleic acids research 39 (12), e79-e79, 2011 | 26 | 2011 |
| Enabling large-scale bioinformatics data analysis with cloud computing J Karlsson, O Torreno, D Ramet, G Klambauer, M Cano, O Trelles 2012 IEEE 10th International Symposium on Parallel and Distributed …, 2012 | 22 | 2012 |
| Interpretable deep learning in drug discovery K Preuer, G Klambauer, F Rippmann, S Hochreiter, T Unterthiner Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 331-345, 2019 | 15 | 2019 |
Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
