no code implementations • 3 Apr 2024 • Shuxian Fan, Adam Visokay, Kentaro Hoffman, Stephen Salerno, Li Liu, Jeffrey T. Leek, Tyler H. McCormick
In this paper, we develop a method for valid inference using outcomes (in our case COD) predicted from free-form text using state-of-the-art NLP techniques.
no code implementations • 14 Jan 2024 • Kentaro Hoffman, Stephen Salerno, Awan Afiaz, Jeffrey T. Leek, Tyler H. McCormick
As artificial intelligence and machine learning tools become more accessible, and scientists face new obstacles to data collection (e. g. rising costs, declining survey response rates), researchers increasingly use predictions from pre-trained algorithms as outcome variables.
no code implementations • 20 Jan 2022 • The Genomic Data Science Community Network, Rosa Alcazar, Maria Alvarez, Rachel Arnold, Mentewab Ayalew, Lyle G. Best, Michael C. Campbell, Kamal Chowdhury, Katherine E. L. Cox, Christina Daulton, Youping Deng, Carla Easter, Karla Fuller, Shazia Tabassum Hakim, Ava M. Hoffman, Natalie Kucher, Andrew Lee, Joslynn Lee, Jeffrey T. Leek, Robert Meller, Loyda B. Méndez, Miguel P. Méndez-González, Stephen Mosher, Michele Nishiguchi, Siddharth Pratap, Tiffany Rolle, Sourav Roy, Rachel Saidi, Michael C. Schatz, Shurjo Sen, James Sniezek, Edu Suarez Martinez, Frederick Tan, Jennifer Vessio, Karriem Watson, Wendy Westbroek, Joseph Wilcox, Xianfa Xie
Over the last 20 years, there has been an explosion of genomic data collected for disease association, functional analyses, and other large-scale discoveries.
no code implementations • 28 Feb 2020 • Benjamin Haibe-Kains, George Alexandru Adam, Ahmed Hosny, Farnoosh Khodakarami, MAQC Society Board, Levi Waldron, Bo wang, Chris McIntosh, Anshul Kundaje, Casey S. Greene, Michael M. Hoffman, Jeffrey T. Leek, Wolfgang Huber, Alvis Brazma, Joelle Pineau, Robert Tibshirani, Trevor Hastie, John P. A. Ioannidis, John Quackenbush, Hugo J. W. L. Aerts
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening.
Applications
no code implementations • 20 May 2019 • Lucy D'Agostino McGowan, Sean Kross, Jeffrey T. Leek
In order to analyze the between researcher variability in data analysis choices as well as the aspects within the data analysis pipeline that contribute to the variability in results, we have created two R packages: matahari and tidycode.
Computation