Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos
Abstract Embryo development is driven by the spatiotemporal dynamics of complex gene regulatory networks. Uncovering these dynamics requires simultaneous tracking of multiple fluctuating molecular species over time, which exceeds the capabilities of traditional live-imaging approaches. Fixed-embryo...
Saved in:
| Main Authors: | Huihan Bao, Shihe Zhang, Zhiyang Yu, Heng Xu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61907-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Uncovering candidate Nanog-Helper genes in early mouse embryo differentiation using differential entropy and network inference
by: Francisco Prista von Bonhorst, et al.
Published: (2025-06-01) -
The impact of data resolution on dynamic causal inference in multiscale ecological networks
by: Erik Saberski, et al.
Published: (2024-11-01) -
Deep-learning model for embryo selection using time-lapse imaging of matched high-quality embryos
by: Lisa Boucret, et al.
Published: (2025-08-01) -
Deciphering the history of ERK activity from fixed-cell immunofluorescence measurements
by: Abhineet Ram, et al.
Published: (2025-05-01) -
Deep learning deciphers the related role of master regulators and G-quadruplexes in tissue specification
by: Artem Bashkatov, et al.
Published: (2025-07-01)