| Revealing strengths and weaknesses of methods for gene network inference D Marbach, RJ Prill, T Schaffter, C Mattiussi, D Floreano, G Stolovitzky Proceedings of the national academy of sciences 107 (14), 6286-6291, 2010 | 734 | 2010 |
| GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods T Schaffter, D Marbach, D Floreano Bioinformatics 27 (16), 2263-2270, 2011 | 460 | 2011 |
| Generating realistic in silico gene networks for performance assessment of reverse engineering methods D Marbach, T Schaffter, C Mattiussi, D Floreano Journal of computational biology 16 (2), 229-239, 2009 | 436 | 2009 |
| Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms T Schaffter, DSM Buist, CI Lee, Y Nikulin, D Ribli, Y Guan, W Lotter, Z Jie, ... JAMA network open 3 (3), e200265-e200265, 2020 | 40 | 2020 |
| Numerical integration of SDEs: a short tutorial T Schaffter | 22 | 2010 |
| The DREAM4 in-silico network challenge D Marbach, T Schaffter, D Floreano, RJ Prill, G Stolovitzky Draft, version 0.3, 2009 | 15 | 2009 |
| Fluorescence Behavioral Imaging (FBI) tracks identity in heterogeneous groups of Drosophila P Ramdya, T Schaffter, D Floreano, R Benton Plos One 7 (11), e48381, 2012 | 14 | 2012 |
| Reproducible biomedical benchmarking in the cloud: lessons from crowd-sourced data challenges K Ellrott, A Buchanan, A Creason, M Mason, T Schaffter, B Hoff, J Eddy, ... Genome biology 20 (1), 1-9, 2019 | 9 | 2019 |
| GNW User Manual T Schaffter, D Marbach, G Roulet | 3 | 2010 |
| The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies JEL Diaz, ME Ahsen, T Schaffter, X Chen, RB Realubit, C Karan, ... Elife 9, e52707, 2020 | 2 | 2020 |
| The Deep Learning Epilepsy Detection Challenge: design, implementation, and test of a new crowd-sourced AI challenge ecosystem I Kiral, S Roy, T Mummert, A Braz, J Tsay, J Tang, U Asif, T Schaffter, ... Challenges in Machine Learning Competitions for All (CiML) 1 (1), 2019 | 2 | 2019 |
| Optimisation d’un moteur synchronea l’aide d’algorithmes génétiques T Schaffter Projet de Semestre, EPFL, 2007 | 2 | 2007 |
| Piloting a model-to-data approach to enable predictive analytics in health care through patient mortality prediction T Bergquist, Y Yan, T Schaffter, T Yu, V Pejaver, N Hammarlund, ... Journal of the American Medical Informatics Association 27 (9), 1393-1400, 2020 | 1 | 2020 |
| GeneNetWeaver User Manual T Schaffter, D Marbach, G Roulet | 1 | 2012 |
| Evaluation of crowdsourced mortality prediction models as a framework for assessing AI in medicine T Bergquist, T Schaffter, Y Yan, T Yu, J Prosser, J Gao, G Chen, ... medRxiv, 2021 | | 2021 |
| From Genes to Organisms T Schaffter EPFL, 2014 | | 2014 |
| GeneNetWeaver 3.0: realistic benchmark generation and performance profiling of network inference methods T Schaffter, D Marbach, K Manolis, D Floreano 3rd annual joint conference on Systems Biology, Regulatory Genomics, and …, 2010 | | 2010 |
| Bio-Inspired Artificial Intelligence S Wischmann, T Schaffter | | 2009 |
| Stochastic Simulations for DREAM4 T Schaffter, D Marbach, O Model | | 2009 |
| GNW User Guide T Schaffter, D Marbach | | 2008 |
Daniel MarbachRoche Pharma Research and Early Development, Basel, SwitzerlandVerified email at roche.com
