2 papers
stat.ME2025
BaGGLS: A Bayesian Shrinkage Framework for Interpretable Modeling of Interactions in High-Dimensional Biological Data
Marta S. Lemanczyk, Lucas Kock, Johanna Schlimme +2
Biological data sets are often high-dimensional, noisy, and governed by complex interactions among sparse signals. This poses major challenges for interpretability and reliable fea…
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…