Latent transcriptional programs reveal histology-encoded tumor features spanning tissue origins
H. M. Hieromnimon, J. Dolezal, K. Doytcheva, F. M. Howard, S. Kochanny, Z. Zhang, R. L. Grossman, K. Tanager, C. Wang, J. N. Kather, E. Izumchenko, N. A. Cipriani, E. J. Fertig, A. T. Pearson, S. J. Riesenfeld
This paper uses machine learning to identify latent transcriptional programs in squamous cell carcinomas from histology images, linking them to clinically relevant biological processes.
bioRxiv · Mar 22, 2023Cancer Biology