We use compact CNNs and transfer learning (pre‑trained on genome‑wide ATAC‑seq and fine‑tuned on validated enhancers from VISTA enhancer browser) to program tissue‑specific enhancers. (2/6)

Transfer learning reveals clear shifts in motif importance between sequence‑to‑accessibility and sequence‑to‑activity models, highlighting the specific features that drive enhancer function. (3/6)

100% (15/15) of designed enhancers for heart, limb, and CNS are active in their intended tissues in mouse embryos. 🎯 (4/6)

We hope this approach will help enable the design of more versatile and precise synthetic enhancers across mammalian tissues, cell types, and dynamic cell states. (5/6)

Grateful for the amazing guidance from @alex-stark.bsky.social and Vincent Loubiere to help me make my first PhD project come ture!
Huge thanks to all co-authors and collaborators, especially Evgeny @evgenykvon.bsky.social and Jacob @jmschreiber91.bsky.social, for making this work possible. 🙏 (6/6)

We use compact CNNs and transfer learning (pre‑trained on genome‑wide ATAC‑seq and fine‑tuned on validated enhancers from VISTA enhancer browser) to program tissue‑specific enhancers. (2/5)

Transfer learning reveals clear shifts in motif importance between sequence‑to‑accessibility and sequence‑to‑activity models, highlighting the specific features that drive enhancer function. (2/5)

100% (15/15) of designed enhancers for heart, limb, and CNS are active in their intended tissues in mouse embryos. 🎯 (3/5)

We hope this approach will help enable the design of more versatile and precise synthetic enhancers across mammalian tissues, cell types, and dynamic cell states. (4/5)

Grateful for the amazing guidance from @alex-stark.bsky.social and Vincent Loubiere to help me make my first PhD project come ture!

Huge thanks to all co-authors and collaborators, especially Evgeny @evgenykvon.bsky.social and Jacob @jmschreiber91.bsky.social, for making this work possible. 🙏 (5/5)