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  1. 🎉 New open-access article in Genome Biology!

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  • 🧪🎊 LLPS benchmarking bonanza!
    • 🌟 What’s inside?
    • 🎯 Why it matters? 🤔
    • 🎉 Kudos to the team
    • 🚀 What’s Next?

🎉 New open-access article in Genome Biology!

publications
LLPS
research
bioinformatics
Our collaborative study led by Carlos is out—providing gold-standard LLPS datasets and evaluation tools! 🧠🧬🎉
Published

July 9, 2025

🧪🎊 LLPS benchmarking bonanza!

We’re thrilled to announce our latest study, Comprehensive protein datasets and benchmarking for liquid–liquid phase separation studies, is now online in Genome Biology! 📘🔥

  • Published: 8 July 2025
  • Lead author: Carlos (huge kudos to Salva’s team!)
  • 🔗 You can read it here

🌟 What’s inside?

LLPS (liquid–liquid phase separation) drives biomolecular condensates, crucial for cellular function. But variability across LLPS databases makes modeling tricky. This study tackles that challenge by:

  • Collecting comprehensive protein datasets across multiple LLPS repositories
  • Creating benchmark frameworks to evaluate predictor performance
  • Defining a reliable set of negative (non-LLPS) proteins for fair model assessment

The goal? To enable transparent, reproducible, and robust ML-driven insights into protein phase behavior.


🎯 Why it matters? 🤔

  • Harmonization: brings together divergent LLPS resources for interoperability
  • Model reliability: improves confidence in ML predictions with curated benchmarks
  • Research enabler: helps the broader community build and validate new tools for condensate biology

By offering standardized datasets and robust benchmarking, this publication sets a new bar for reproducible ML in LLPS research.


🎉 Kudos to the team

Special shout-out to Carlos for leading the project with Oriol, Eva, Valen, Michał and Salva! Your teamwork made this possible 👏


🚀 What’s Next?

We’re already leveraging these benchmarking datasets in our ongoing LLPS and amyloid research.. If you’re into biomolecular condensates or predictive modeling, this resource is for you!


🎉 Let’s continue driving reproducible and data-driven science in LLPS research. Congrats, team! 🥳

 

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