🔥 Super cool research in longevity by Shift Bioscience! A new preprint ("A single factor for safer cellular rejuvenation") from Shift Bioscience reports a striking finding: a single gene, SB000, achieves robust cellular rejuvenation across two germ layers—fibroblasts and keratinocytes—without inducing pluripotency. This could mark a meaningful step toward safer partial reprogramming. Using transcriptomic aging clocks, the authors show that SB000 reverses age-associated gene expression, reduces senescence markers, and maintains cell identity—outperforming OSK across dozens of epigenetic clocks, including Horvath and DunedinPACE. This matters. While OSK-based reprogramming has dominated the field, its translational barriers—tumorigenicity, dedifferentiation, and delivery—remain high. A factor that reverses biological age without disrupting cell function could change the game. But perhaps the deeper story here is how they got there. This discovery wasn’t driven by a one-at-a-time gene screen. It relied on a scalable, high-throughput computational pipeline—a blueprint for how AI-first biology can unlock novel rejuvenation strategies. This is exactly the promise of foundation models like scGPT: large-scale pretraining on single-cell atlases to learn the latent rules of gene regulation, cell state, and age progression. These models can predict perturbation effects, identify rejuvenation signatures, and even suggest interventions de novo. The convergence is clear: --AI enables global search over the gene regulatory space. A one-at-a-time gene screen didn’t drive this discovery --Foundation standards contextualize interventions across tissues, age, and health status. --Experimental biology validates, refines, and feeds back into the loop. Shift's SB000 is an exciting advance. But it’s also a case study of where aging research is going: from hypothesis-driven, to model-guided; from single-gene overexpression, to generative design grounded in cellular language. The future of aging is being written in code—both genetic and computational. Congratulations to the whole shift bio team! @lucascamillomd @dives86 @BrendanMSwain #AIinBiology #Longevity #Reprogramming #AgingResearch #scGPT #FoundationModels

🔥 Unveiling the Future of Genomics with Genome Language Models (gLMs)! 🔥 Our comprehensive review, "Transformers and genome language models," is finally published in Nature Machine Intelligence! ​ Link: nature.com/articles/s42256-0… Key Highlights: 🔬 The Challenges Addressed by gLMs: gLMs tackle the intricate task of interpreting vast genomic sequences, enabling predictions about gene regulation, variant effects, and more.​ 🧠 Transformers in Genomics: Discover how transformer architectures, renowned for their success in natural language processing, are adept at capturing long-range dependencies in genomic data, leading to more accurate models.​ 🚀 Beyond Transformers—Introducing HyenaDNA: Explore innovative architectures like HyenaDNA, which offer efficient long-range genomic sequence modeling at single nucleotide resolution, pushing the boundaries of genomic research.​ 📊 Comparative Analysis of Models: We delve into the evolution from sequence-to-function models like DeepSEA and Enformer to sequence-to-sequence models such as DNABERT and Evo, highlighting their respective strengths and applications.​ ⚡ Strengths, Limitations, & Future Directions: Gain insights into the current capabilities of genomic AI, its limitations, and the promising avenues for future research and application.​ This pivotal work is the result of a collaborative effort led by Micaela E. Consens (@micaelanonsense ), with contributions from Cameron Dufault, Michael Wainberg (@michaelwainberg ), Duncan Forster, Mehran Karimzadeh, Hani Goodarzi (@genophoria ), Fabian J. Theis (@fabian_theis ), Alan Moses. @UHNAIHUB @UHN @VectorInst @uoftoront #Genomics #AI #MachineLearning #Transformers #HyenaDNA #DeepLearning #Bioinformatics #GenomeResearch