Our latest study "Machine learning prediction of prime editing efficiency across diverse chromatin contexts" is out @NatureBiotech! Shout out to our awesome collaborators @nicopmat @schwanklab @bvansteensellab and all (co)-authors 👏 https://t.co/G5C7CgpSQ5 @UZH_Science @DQBM_uzh

Our preprint "TAMER: A Test-Time Adaptive MoE-Driven Framework for EHR Representation Learning" is out! A great work by @yhzhu99 @XiaochenZheng3 👏 Paper: https://t.co/8MDD76pJ4a #MachineLearning #Healthcare @UZH_en @DQBM_uzh

🚀 Wrapping up 2024 with a bang! Check out our exciting ArXiv paper: "Explainable Multi-Modal Data Exploration in Natural Language via LLM Agent." 🌟 AI just got smarter and more accessible. Here's to 2025! 🎉 #Multimodality #AgenticAI #ExplainableAI #LLMs https://t.co/RIjiWLHIhB

@DQBM_uzh @Unispital_USZ @UZH_en

Please RT and share widely! #MachineLearning #healthcare #phd #ArtificialIntelligence #biomedicine #oncology

𝗘𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗣𝗵𝗗 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆 𝗶𝗻 𝗔𝗜 𝗳𝗼𝗿 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗢𝗻𝗰𝗼𝗹𝗼𝗴𝘆 in Our Lab! Candidate will work on AI-driven precision oncology, developing solutions that could revolutionize how we approach cancer treatment. Apply 👉 https://t.co/Oo7hSW62vF

I am hiring a PhD student to join our team at the Medical Informatics at UZH and work on an exciting SNSF project starting early next year. SNF PhD position in Medical NLP Please share 👍🙌... https://t.co/zh1DIfYS3n #uzh, #krauthammerlab

💫We are hiring a PhD student to join us and work on an exciting SNSF project starting early next year. "SNF PhD position in Medical Informatics with a Focus on Natural Language Processing" Please share 🙌 https://t.co/7DAJP9XcK6 @farhadnz

Bridging genome editing, protein engineering, and applied machine learning in biology: Great to see our latest study, spearheaded by @marquark, out in @naturemethods. Check out the paper! 🧬📷 https://t.co/hANtAKwFEm @nicopmat @schwanklab @krauthammerlab

And read the Research Briefing on the work here: https://t.co/tfT2FyOl8a

.@schwanklab and colleagues describe TnpB variants with increased activity and a broader targeting range, along with a deep learning model to predict ωRNA efficiency. @marquark @nicopmat @Amina_mollaysa @krauthammerlab https://t.co/lakYFNn3ot

If you're more of a visual learner, this explainer video is for you 🙌

Our TnpB story has been published in @naturemethods 🚀 Check out the thread by @schwanklab to dive right in.

TnpB is much smaller than the well-known CRISPR-Cas "gene scissors". The tool for targeted modification of the genome has now been improved to such an extent that it could one day be used to treat a genetic defect that leads to a lifelong high cholesterol level.

TnpB ist viel kleiner als die bekannte «Genschere» CRISPR-Cas. Nun wurde das Tool zur gezielten Veränderung des Genoms so verbessert, dass es dereinst zur Behandlung eines Gendefekts eingesetzt werden könnte, der zu einem lebenslang stark erhöhten Cholesterinspiegel führt.

Check the @naturemethods research briefing associated with our latest study on TnpB for effective genome editing https://t.co/zmIfUECkxo

Our latest collab with @schwanklab is out today! "Effective genome editing with an enhanced ISDra2 TnpB system and deep learning-predicted ωRNAs" https://t.co/K5EfaMgOKB Congrats to all authors & collaborators!👏🎉 @marquark @Amina_mollaysa @DQBM_uzh @Unispital_USZ @UZH_H2R

Bridging genome editing, protein engineering, and applied machine learning in biology: Great to see our latest study, spearheaded by @marquark, out today in @naturemethods. Check out the paper! 🧬💻 @UZH_Science @schwanklab @krauthammerlab

@UZH_Science @DQBM_uzh #MachineLearning #GenerativeAI #MolecularDesign

Our work "Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Enhanced Goal Directed Generation" got accepted at the ICML 2024 Workshop AccMLBio! Great work by Arthur-Louis Heath and @Amina_mollaysa 👏🎉! 🗞️ https://t.co/sIFGryCcfa

'Explainable deep learning for disease activity prediction in chronic inflammatory joint diseases' is now out in @PLOSDigiHealth @cecile_tt @CarolineOspelt 👏🎉 Paper: