Unsupervised domain adaptation methods for cross-species transfer of regulatory code signals.

ABSTRACT: Due to advances in NGS technologies whole-genome maps of various functional genomic elements were generated for a dozen of species, however experiments are still expensive and are not available for many species of interest. Deep learning methods became the state-of-the-art computational methods to analyze the available data, but the focus is often only on the species studied. Here we take advantage of the progresses in Transfer Learning in the area of Unsupervised Domain Adaption (UDA) and tested nine UDA methods for prediction of regulatory code signals for genomes of other species. We tested each deep learning implementation by training the model on experimental data from one species, then refined the model using the genome sequence of the target species for which we wanted to make predi

SUBMITTER: Latyshev P 

PROVIDER: S-EPMC10101332 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature