| Self-normalizing neural networks G Klambauer, T Unterthiner, A Mayr, S Hochreiter Advances in neural information processing systems 30, 2017 | 3493 | 2017 |
| DeepTox: toxicity prediction using deep learning A Mayr, G Klambauer, T Unterthiner, S Hochreiter Frontiers in Environmental Science 3, 80, 2016 | 1004 | 2016 |
| 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 | 550 | 2018 |
| 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-e69, 2012 | 506 | 2012 |
| FABIA: factor analysis for bicluster acquisition S Hochreiter, U Bodenhofer, M Heusel, A Mayr, A Mitterecker, A Kasim, ... Bioinformatics 26 (12), 1520-1527, 2010 | 397 | 2010 |
| Speeding up semantic segmentation for autonomous driving M Treml, J Arjona-Medina, T Unterthiner, R Durgesh, F Friedmann, ... | 353 | 2016 |
| Deep learning as an opportunity in virtual screening T Unterthiner, A Mayr, G Klambauer, M Steijaert, JK Wegner, ... Proceedings of the deep learning workshop at NIPS 27, 1-9, 2014 | 248 | 2014 |
| Toxicity prediction using deep learning T Unterthiner, A Mayr, G Klambauer, S Hochreiter arXiv preprint arXiv:1503.01445, 2015 | 129 | 2015 |
| Prediction of human population responses to toxic compounds by a collaborative competition F Eduati, LM Mangravite, T Wang, H Tang, JC Bare, R Huang, T Norman, ... Nature biotechnology 33 (9), 933-940, 2015 | 121 | 2015 |
| Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project B Verbist, G Klambauer, L Vervoort, W Talloen, Z Shkedy, O Thas, ... Drug discovery today 20 (5), 505-513, 2015 | 111 | 2015 |
| Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks M Hofmarcher, A Mayr, E Rumetshofer, P Ruch, P Renz, J Schimunek, ... arXiv preprint arXiv:2004.00979, 2020 | 63 | 2020 |
| Furby: fuzzy force-directed bicluster visualization M Streit, S Gratzl, M Gillhofer, A Mayr, A Mitterecker, S Hochreiter BMC bioinformatics 15, 1-13, 2014 | 62 | 2014 |
| Industry-scale application and evaluation of deep learning for drug target prediction N Sturm, A Mayr, T Le Van, V Chupakhin, H Ceulemans, J Wegner, ... Journal of Cheminformatics 12, 1-13, 2020 | 48 | 2020 |
| Cross-Domain Few-Shot Learning by Representation Fusion T Adler, J Brandstetter, M Widrich, A Mayr, D Kreil, M Kopp, G Klambauer, ... arXiv preprint arXiv:2010.06498, 2020 | 47 | 2020 |
| Boundary graph neural networks for 3d simulations A Mayr, S Lehner, A Mayrhofer, C Kloss, S Hochreiter, J Brandstetter Proceedings of the AAAI Conference on Artificial Intelligence 37 (8), 9099-9107, 2023 | 36 | 2023 |
| DeepTox: Toxicity prediction using deep learning G Klambauer, T Unterthiner, A Mayr, S Hochreiter Toxicol Lett 280, S69-S69, 2017 | 31 | 2017 |
| Application of bioactivity profile-based fingerprints for building machine learning models N Sturm, J Sun, Y Vandriessche, A Mayr, G Klambauer, L Carlsson, ... Journal of chemical information and modeling 59 (3), 962-972, 2018 | 30 | 2018 |
| 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 | 29 | 2011 |
| ELU-networks: fast and accurate CNN learning on imagenet M Heusel, DA Clevert, G Klambauer, A Mayr, K Schwarzbauer, ... NiN 8, 35.68, 2015 | 28 | 2015 |
| Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map G Klambauer, M Wischenbart, M Mahr, T Unterthiner, A Mayr, ... Bioinformatics 31 (20), 3392-3394, 2015 | 25 | 2015 |
Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
