Our Systema framework for evaluating genetic perturbation response prediction methods is now out in @natbiotech.nature.com ✨
Systema helps to evaluate perturbation response prediction methods by focusing on perturbation-specific effects rather than systematic variation 🎯
Very excited to announce that the single cell/nuc. RNA/ATAC/multi-ome resource from ENCODE4 is now officially public. This includes raw data, processed data, annotations and pseudobulk products. Covers many human & mouse tissues. 1/
www.encodeproject.org/single-cell/...
Thrilled to share LUNA🌕 – our new generative AI model that reassembles tissue structures from dissociated cells! LUNA learns spatial priors over existing spatially resolved datasets with the aim to predict cell locations de novo.
Check out our paper here: www.biorxiv.org/content/10.1...
Maria Brbic
🚀 We're #Hiring #PhD students at #MLBio Lab at
@EPFL through the #EDIC Program!
📅 Deadline: Dec 15
💥 We're part of School of Computer Science and School of Life Sciences at EPFL which gives access to excellent collaborators, network, and resources!
👉 Learn more at brbiclab.epfl.ch!
Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation - @mariabrbic.bsky.social @mornitzan.bsky.social go.nature.com/4791kda
Honored to work with the exceptional @weiqiu.bsky.social @uwcse.bsky.social and brilliant co-senior author Kamila Naxerova @harvardmed.bsky.social! I still can’t believe I have the privilege of collaborating with them www.nature.com/articles/s41...
@weiqiu.bsky.social is on the academic job market!
🎉 Four of our faculty members have been awarded the prestigious Swiss National Science Foundation SNSF "Starting Grant" for their groundbreaking research projects 🌟
Congratulations to @mariabrbic.bsky.social, Josie Hughues
@trackingactions.bsky.social and Philippe Schwaller! 🥳
💥 Excited to introduce Bacformer 🦠 - the first foundation model for bacterial genomics. Bacformer represents genomes as sequences of ordered proteins, learning the “grammar” of how genes are arranged, interact and evolve.
Preprint 📝: biorxiv.org/content/10.1...
🧵 1/n
