Thomas Schaffter
Thomas Schaffter
Sage Bionetworks
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Cited by
Cited by
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
GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods
T Schaffter, D Marbach, D Floreano
Bioinformatics 27 (16), 2263-2270, 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
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
Numerical integration of SDEs: a short tutorial
T Schaffter
The DREAM4 in-silico network challenge
D Marbach, T Schaffter, D Floreano, RJ Prill, G Stolovitzky
Draft, version 0.3, 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
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
GNW User Manual
T Schaffter, D Marbach, G Roulet
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
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
Optimisation d’un moteur synchronea l’aide d’algorithmes génétiques
T Schaffter
Projet de Semestre, EPFL, 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
GeneNetWeaver User Manual
T Schaffter, D Marbach, G Roulet
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
From Genes to Organisms
T Schaffter
EPFL, 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
Bio-Inspired Artificial Intelligence
S Wischmann, T Schaffter
Stochastic Simulations for DREAM4
T Schaffter, D Marbach, O Model
GNW User Guide
T Schaffter, D Marbach
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