T. Schaffter

发表

Revealing strengths and weaknesses of methods for gene network inference

D. Floreano, C. Mattiussi, D. Marbach, 2010, Proceedings of the National Academy of Sciences.

GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods

Dario Floreano, Daniel Marbach, Thomas Schaffter, 2011, Bioinform..

Numerical Integration of SDEs: A Short Tutorial

T. Schaffter, 2010 .

Fluorescence Behavioral Imaging (FBI) Tracks Identity in Heterogeneous Groups of Drosophila

D. Floreano, T. Schaffter, Pavan Ramdya, 2012, PloS one.

Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods

Dario Floreano, Daniel Marbach, Claudio Mattiussi, 2009, J. Comput. Biol..

GeneNetWeaver 3.0: realistic benchmark generation and performance profiling of network inference methods

Dario Floreano, Daniel Marbach, Thomas Schaffter, 2010 .

Evaluation of crowdsourced mortality prediction models as a framework for assessing AI in medicine

S. Mooney, T. Schaffter, J. Guinney, 2021, medRxiv.

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Berkman Sahiner, Jaime S. Cardoso, Thomas Yu, 2020, JAMA network open.

The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies

A. Califano, T. Schaffter, G. Stolovitzky, 2020, eLife.

The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies

A. Califano, T. Schaffter, G. Stolovitzky, 2019, bioRxiv.

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification pdf

Christos Davatzikos, Russell T. Shinohara, Spyridon Bakas, 2021, ArXiv.

The NLP Sandbox: an efficient model-to-data system to enable federated and unbiased evaluation of clinical NLP models

James A. Eddy, Nicholas J. Dobbins, S. Mooney, 2022, ArXiv.

External Validation of an Ensemble Model for Automated Mammography Interpretation by Artificial Intelligence

Christoph I. Lee, William Lotter, J. Elmore, 2022, JAMA network open.

Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data

Hasan A. Poonawala, Todd W. Mummert, T. Schaffter, 2021, EBioMedicine.

Evaluation of Combined Artificial Intelligence and Neurologist Assessment to Annotate Scalp Electroencephalography Data

Hasan A. Poonawala, Todd W. Mummert, T. Schaffter, 2020 .

From Genes to Organisms

Thomas Schaffter, T. Schaffter, 2014 .

Reproducible biomedical benchmarking in the cloud: lessons from crowd-sourced data challenges

Thomas Yu, Justin Guinney, Julio Saez-Rodriguez, 2019, Genome Biology.

Evaluation of crowdsourced mortality prediction models as a framework for assessing artificial intelligence in medicine.

L. Kaderali, M. Vollmer, P. Heagerty, 2023, Journal of the American Medical Informatics Association : JAMIA.

An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C)

Andrew E. Williams, C. Chute, Hongfang Liu, 2021, J. Am. Medical Informatics Assoc..

Piloting a model-to-data approach to enable predictive analytics in health care through patient mortality prediction

Thomas Yu, Vikas Pejaver, Sean Mooney, 2020, J. Am. Medical Informatics Assoc..