❗ Beware of 'AI-washed' products in drug discovery and data analysis!❗ True innovation isn't about slapping an AI label on old tech. The Spring Engine represents genuine AI advancement, accelerating drug discovery like never before.

At Spring Discovery, we're not just using AI – we're redefining it for drug discovery. Our cutting-edge embeddings technology is years ahead of the competition. Discover how we're revolutionizing image analysis: buff.ly/4dLZ6jQ #AIInnovation #DrugDiscovery

🎃 Not all cells go quietly… ☠️ Some die with a bang, others with a whimper, and Spring’s AI tools capture every haunting detail. Let’s dive into the eerie world of cell death mechanisms. (1/4)

6/ Learn more about how we’re adopting these methods and our work on live-cell and label-free analysis approaches. Check out our latest blog here: 🔗 buff.ly/4e0LOzU Stay tuned for more exciting updates from @SpringScienceAI! 👀 #LifeSciences #DrugDiscovery

We love cute animal pictures as much as the next person, but they won’t help us discover new therapies. We use real cell imaging data for that! 🧬🐱🐶 #BioEmbeddings #AI #NotYourAverageAI

Building on our previous post about the Taipale lab’s work, 16% of pathogenic variants are linked to protein mislocalization, disrupting stability, especially in the secretory pathway. 🧬 Variants with distinct localization patterns are tied to different disease outcomes. 🧵

🚨 New research from the Taipale Lab @mike_tilapia reveals that protein mislocalization impacts 1/6 of all pathogenic missense variants! 🧬 This study offers new insights into the molecular roots of genetic disorders. 📊 Data now available on the Spring Science platform! 🧵

For a teaser, we've included an image of some of our PBMC cells stained with Hoechst(blue), mitotracker(red) and phalloidin (green). Check it out here! buff.ly/3wHOjr9

Happy #fluorescencefriday! This week, instead of dropping just one fluorescent image in your feed, we're providing the ability to look at more! We have a public #aging workspace, where you can explore our historical PBMC aging data in our platform.

Welcome to 2024! Many companies slap 'AI' on old tools – we call it the 'AI band-aid.' They're stuck fixing segmentation errors instead of advancing science. There's a better way with improved prediction accuracy. What if you didn't have to tweak cell segmentation settings?