Theodoris Lab

Scientific American used expert recommendations and data analysis to identify 28 exceptional early-career researchers spklr.io/6011E3l9W

Happy to share a protocol for discovery of candidate therapeutic targets with Geneformer by @YujieZh1729! Nature Protocols link: rdcu.be/ffApo Google Colab: tinyurl.com/geneformertuto…

Really enjoyed talking with science journalist Andrew Han about art and science on his new podcast, Ion Genomics! Check it out here: iongenomics.bio/p/podcast-epis…

Thank you to the whole team for their wonderful collaboration on this work! Javier Gόmez Ortega, @alexis_combes, @andcyang, @StefanieDimmel1, Shinya Yamanaka, @m_alexanian, @NVIDIAHealth

Manuscript: tinyurl.com/maxtoki Press release: gladstone.org/news/new-ai-mo… Model: huggingface.co/theodoris-lab/… Code: github.com/NVIDIA-Digital… Blog post: theodorislab.gladstone.org/post/maxtoki

Excited to share MaxToki, a temporal AI model that predicts the impact of perturbations on cell states over time along dynamic trajectories. We applied MaxToki to predict how cells age across the human lifespan & discovered new cardiac pro-aging drivers that we validated in vivo.

Thank you to the whole team on this collaboration: Javier Gómez Ortega, @Sid_Mahesh_12, Tarak Nandi, @madduri, and @PelkaLab!

Congrats to @hanimal725 and @madhavanvvs for their work in Nature Computational Science that demonstrated scaling laws for foundation models for network biology and implemented a quantization approach to enable resource-efficient predictions! rdcu.be/famFk

Thank you to the whole team for the wonderful collaboration! Javier Gómez Ortega, @Sid_Mahesh_12, Tarak Nandi, @madduri, and @PelkaLab! x.com/TheodorisLab/s…

Overall, quantized multi-task learning enables resource-efficient context-specific modeling in gene network biology to yield contextual predictions of key network regulators and candidate therapeutic targets for human disease.

We are excited to share work led by @hanimal725 and @madhavanvvs developing a quantized multitask learning strategy for network biology, built on a foundation model pretrained on ~95M single-cell transcriptomes. Manuscript: tinyurl.com/qmtl-gf Model: huggingface.co/ctheodoris/Gen…