Deep learning DNA sequence-to-function models offer the promise of gaining mechanistic insights into genome regulation, however their performance is often limited by data scarcity in the species of interest. We present DanioDecima, a zebrafish-specific model leveraging transfer learning from human and mouse-trained models to predict tissue- and cell-type-specific gene expression during zebrafish embryogenesis. Initializing DanioDecima with pretrained human and mouse Borzoi and Decima weights raises the median pseudobulk Pearson r substantially across cell-types and improves gene-level correlations of test set genes. An in silico directed-evolution loop guided by DanioDecima scoring generated synthetic promoters whose motif architectures cluster by the expected target lineage. These findings exemplify a cross-species transfer learning methodology for sequence-to-function models, and position DanioDecima as a practical resource for zebrafish regulatory engineering.
Target-Side Paraphrase Augmentation for Sign Language Translation with Large Language Models
arXiv:2605.31393v1 Announce Type: cross Abstract: Sign language translation (SLT) remains constrained by limited paired sign-video/text corpora and heavy-tailed target vocabularies. We study target-side augmentation in