2 papers
cs.LG2026
Uncertainty-Aware Deep Learning for Genomics Applications: Insights from an Empirical Study
Sepideh Saran, Mahsa Ghanbari, Uwe Ohler
Deep learning models have emerged as the standard computational tool for a wide range of applications in genomics. Yet, uncertainty quantification (UQ) -- and more specifically, th…
q-bio.GN2024
Metadata-guided Feature Disentanglement for Functional Genomics
Alexander Rakowski, Remo Monti, Viktoriia Huryn +3
With the development of high-throughput technologies, genomics datasets rapidly grow in size, including functional genomics data. This has allowed the training of large Deep Learni…