We were able to reliably predict cell fate from the very first frame ⚡️of 3 days long acquisition, and identify relevant biological processes🧬 involved in cancer persister cells, despite their known biological variability (3/n)

We propose a fully data-driven approach to early cell fate prediction from a microscopy time-lapse. We combine an adversarial autoencoder architecture with a multi-objective loss function, including contrastive learning, to optimise both cell fate prediction and interpretability (2/n)

Our work “AI4CellFate: Interpretable Early Cell Fate Prediction with Generative AI” is finally out in
@biorxivpreprint.bsky.social !!! 😍❤️ biorxiv.org/content/10.1... @scilifelab.se , @crick.ac.uk . More about it in the thread #AI4CellFate (1/n)⬇️

First research manuscript of my PhD finally submitted!! 🥳❤️ Preprint coming soon... 👀 @juliettegriffie.bsky.social

What an incredible journey with Bruno and António (not on bsky yet) ❤️ I learned so much with these guys and had a blast working with them. This was truly the dream team ❤️ #NanoPyx

What methods does #NanoPyx offer? We have Image Registration, Denoising, Super-Resolution Image Reconstruction🔬 and Quality Control metrics! (4/n)

The #LiquidEngine has an AI-powered agent 🤖 that predicts the fastest way of running a specific analysis task, given the input data, the user hardware and task parameters. Incorporating the #LiquidEngine significantly improves the performance of bioimage analysis workflows!(3/n)

The fastest way of performing any analysis task depends on the user device 💻, the input data 🦠 and the specific task we’re trying to run (2/n).

And to start 2025 strong… Our work #NanoPyx is out in Nature Methods 😍❤️ DOI: doi.org/10.1038/s415...
#NanoPyx is a Python framework that uses the #LiquidEngine to efficiently accelerate bioimage analysis for any user! (1/n) ⬇️