| 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 | 887 | 2010 |
| The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification U Baid, S Ghodasara, S Mohan, M Bilello, E Calabrese, E Colak, ... arXiv preprint arXiv:2107.02314, 2021 | 672 | 2021 |
| GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods T Schaffter, D Marbach, D Floreano Bioinformatics 27 (16), 2263-2270, 2011 | 658 | 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 | 544 | 2009 |
| The National COVID Cohort Collaborative (N3C): rationale, design, infrastructure, and deployment MA Haendel, CG Chute, TD Bennett, DA Eichmann, J Guinney, WA Kibbe, ... Journal of the American Medical Informatics Association 28 (3), 427-443, 2021 | 459 | 2021 |
| 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 | 366 | 2020 |
| The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv 2021 U Baid, S Ghodasara, S Mohan, M Bilello, E Calabrese, E Colak, ... arXiv preprint arXiv:2107.02314, 2021 | 59 | 2021 |
| Numerical integration of SDEs: a short tutorial T Schaffter | 30 | 2010 |
| External validation of an ensemble model for automated mammography interpretation by artificial intelligence W Hsu, DS Hippe, N Nakhaei, PC Wang, B Zhu, N Siu, ME Ahsen, ... JAMA network open 5 (11), e2242343-e2242343, 2022 | 27 | 2022 |
| 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, 1-9, 2019 | 26 | 2019 |
| 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 | 24 | 2020 |
| The DREAM4 in-silico network challenge D Marbach, T Schaffter, D Floreano, RJ Prill, G Stolovitzky Draft, version 0.3, 2009 | 23 | 2009 |
| Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data S Roy, I Kiral, M Mirmomeni, T Mummert, A Braz, J Tsay, J Tang, U Asif, ... EBioMedicine 66, 2021 | 18 | 2021 |
| 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 |
| 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 | 13 | 2020 |
| From genes to organisms: Bioinformatics system models and software T Schaffter EPFL, 2014 | 13 | 2014 |
| A continuously benchmarked and crowdsourced challenge for rapid development and evaluation of models to predict COVID-19 diagnosis and hospitalization Y Yan, T Schaffter, T Bergquist, T Yu, J Prosser, Z Aydin, A Jabeer, ... JAMA Network Open 4 (10), e2124946-e2124946, 2021 | 12 | 2021 |
| An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C) S Liu, A Wen, L Wang, H He, S Fu, R Miller, A Williams, D Harris, ... Journal of the American Medical Informatics Association 30 (12), 2036-2040, 2023 | 7 | 2023 |
| Evaluation of combined artificial intelligence and neurologist assessment to annotate scalp electroencephalography data S Roy, I Kiral-Kornek, M Mirmomeni, T Mummert, A Braz, J Tsai, J Tang, ... EBioMedicine 66, 103275, 2021 | 6 | 2021 |
| 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, 2021.01. 18.21250072, 2021 | 5 | 2021 |
Daniel MarbachRoche Pharma Research and Early Development, Basel, SwitzerlandEmail verificata su roche.com
