0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Join our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessThis work was also supported by a sponsored research agreements from Navigate Biopharma and NextCure and by grants from the NIH including the Yale SPORE in in Skin Cancer, P50 CA121974, the Yale SPORE in Lung Cancer, P50 CA196530, NYU SPORE in Skin Cancer P50CA225450 and the Yale Cancer Center Support Grant, P30CA016359.
Thazin Nwe Aung, Saba Shafi, James S. Wilmott, Saeed Nourmohammadi, Ioannis Vathiotis, Niki Gavrielatou, Aileen I. Fernandez, Vesal Yaghoobi, Tobias Sinnberg, Teresa Amaral, Kristian Ikenberg, Kiarash Khosrotehrani, Iman Osman, Balázs Ács, Yalai Bai, Sandra Martínez-Morilla, Myrto Moutafi, John F. Thompson, Richard A Scolyer, David L. Rimm (2022). Objective assessment of tumor infiltrating lymphocytes as a prognostic marker in melanoma using machine learning algorithms. , 82, DOI: https://doi.org/10.1016/j.ebiom.2022.104143.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2022
Authors
20
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1016/j.ebiom.2022.104143
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free AccessYes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaboration
