Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Kumbier, Karl"'
Simulations play a crucial role in the modern scientific process. Yet despite (or due to) their ubiquity, the Data Science community shares neither a comprehensive definition for a "high-quality" study nor a consolidated guide to designing one. Inspi
Externí odkaz:
http://arxiv.org/abs/2403.08971
Autor:
Kumbier, Karl, Roth, Maike, Li, Zizheng, Lazzari-Dean, Julia, Waters, Christopher, Hammerlindl, Sabrina, Rinaldi, Capria, Huang, Ping, Korobeynikov, Vladislav A., Phatnani, Hemali, Shneider, Neil, Jacobson, Matthew P., Wu, Lani F., Altschuler, Steven J.
Publikováno v:
In Developmental Cell 19 August 2024 59(16):2134-2142
Autor:
Behr, Merle1 (AUTHOR), Kumbier, Karl2 (AUTHOR), Cordova-Palomera, Aldo3 (AUTHOR), Aguirre, Matthew3,4 (AUTHOR), Ronen, Omer5 (AUTHOR), Ye, Chengzhong5 (AUTHOR), Ashley, Euan6 (AUTHOR), Butte, Atul J.7 (AUTHOR), Arnaout, Rima7,8 (AUTHOR), Brown, Ben5,9 (AUTHOR), Priest, James3 (AUTHOR) jpriest@stanford.edu, Yu, Bin5,10 (AUTHOR) jpriest@stanford.edu
Publikováno v:
PLoS ONE. 4/16/2024, Vol. 19 Issue 4, p1-23. 23p.
Autor:
Altieri, Nick, Barter, Rebecca L., Duncan, James, Dwivedi, Raaz, Kumbier, Karl, Li, Xiao, Netzorg, Robert, Park, Briton, Singh, Chandan, Tan, Yan Shuo, Tang, Tiffany, Wang, Yu, Zhang, Chao, Yu, Bin
Publikováno v:
Published in Harvard Data Science Review, 2020
As the COVID-19 outbreak evolves, accurate forecasting continues to play an extremely important role in informing policy decisions. In this paper, we present our continuous curation of a large data repository containing COVID-19 information from a ra
Externí odkaz:
http://arxiv.org/abs/2005.07882
Tree ensembles such as Random Forests have achieved impressive empirical success across a wide variety of applications. To understand how these models make predictions, people routinely turn to feature importance measures calculated from tree ensembl
Externí odkaz:
http://arxiv.org/abs/1906.10845
Autor:
Yu, Bin, Kumbier, Karl
Building and expanding on principles of statistics, machine learning, and scientific inquiry, we propose the predictability, computability, and stability (PCS) framework for veridical data science. Our framework, comprised of both a workflow and docu
Externí odkaz:
http://arxiv.org/abs/1901.08152
Publikováno v:
Published in PNAS 2019
Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receivin
Externí odkaz:
http://arxiv.org/abs/1901.04592
Autor:
Rinaldi, Capria, Waters, Christopher S., Li, Zizheng, Kumbier, Karl, Rao, Lee, Nichols, R. Jeremy, Jacobson, Matthew P., Wu, Lani F., Altschuler, Steven J.
Publikováno v:
In Cell Reports 30 May 2023 42(5)
Autor:
Kumbier, Karl, Basu, Sumanta, Frise, Erwin, Celniker, Susan E., Brown, James B., Celniker, Susan, Yu, Bin
Standard ChIP-seq peak calling pipelines seek to differentiate biochemically reproducible signals of individual genomic elements from background noise. However, reproducibility alone does not imply functional regulation (e.g., enhancer activation, al
Externí odkaz:
http://arxiv.org/abs/1810.07287
Autor:
Yu, Bin, Kumbier, Karl
Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during generation of data, development of algorithms, and evaluation of results. This paper discusses
Externí odkaz:
http://arxiv.org/abs/1712.03779