1/ How can we model each tumor’s unique biology instead of relying on one-size-fits-all approaches? In our latest blog post, we highlight findings from the recent PNAS paper with GenBio AI Research Scientist @probablybots and Co-Founder and Chief Scientist @ericxing, showing how contextualized networks pave the way for an interactome module in AIDO.

2/ Traditional methods miss patient-specific details. Contextualized models infer sample-specific GRNs, unlocking a more precise understanding of tumor biology.

3/ Why this matters for AIDO: • Generating personalized models on-demand for every individual • Unified ML paradigm for multi-modal data integration • Information flow from molecular to cellular scale improves disease models Paper 🔗 pnas.org/doi/10.1073/pn…

4/ This work strengthens AIDO’s vision: multiscale foundation models for predicting, simulating, and programming biology across contexts, from molecules to phenotypes. Read more ↓ genbio.ai/aido-interacto…

@genbioai Great question! Personalized tumor models using AI can truly revolutionize treatment by capturing each tumor’s unique biology. Looking forward to reading the blog and seeing how GenBio AI is pushing this forward!

@genbioai i might be missing it, but how do you resolve GRN vs imaging conflicts per sample?

@genbioai This multi-modal approach to personalized cancer models is fascinating! The ability to integrate molecular to cellular scale data could really revolutionize precision oncology. Looking forward to seeing AIDO's applications in clinical settings! 🧬

@genbioai Next level science, boss

@genbioai AIDO让AI医疗更懂你

@genbioai Always delivering 🌟

@genbioai be well tested

@genbioai on-demand personalized models + multi-modal fusion could sharpen AIDO insights

@genbioai Good