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Thomas Schaffter
Thomas Schaffter
Sage Bionetworks
Verified email at sagebionetworks.org - Homepage
Title
Cited by
Cited by
Year
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
8772010
GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods
T Schaffter, D Marbach, D Floreano
Bioinformatics 27 (16), 2263-2270, 2011
6362011
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
5342009
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
4302021
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
3902021
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
3122020
Numerical integration of SDEs: a short tutorial
T Schaffter
312010
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
242020
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
242019
The DREAM4 in-silico network challenge
D Marbach, T Schaffter, D Floreano, RJ Prill, G Stolovitzky
Draft, version 0.3, 2009
232009
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
162022
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
142021
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
142012
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
112021
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
112020
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
52021
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
42021
An open natural language processing development 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, ...
arXiv preprint arXiv:2110.10780, 2021
32021
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
32019
GNW User Manual
T Schaffter, D Marbach, G Roulet
32010
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Articles 1–20