Theodoris Lab @TheodorisLab
mapping gene networks to enable network-based therapeutic discovery in cardiovascular disease Gladstone Institutes Joined September 2022-
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Thrilled to release two new preprints on intelligent labs for driving science and innovation. This is in close coordination with Aviv Regev, Jian Ma (@jmuiuc), Michelle Lee (@michellearning), and the teams at @Genentech, @SCSatCMU, and @Princeton University. In our perspective, we argue that the next generation of labs should be human-in-the-lead and AI-empowered, integrating human intent, machine reasoning, and physical experimentation through scientific world models and an agentic harnessing layer. Done responsibly, these systems have the potential to make scientific discovery more programmable, reproducible, adaptive, and scalable while enabling scientists to focus on higher-level scientific reasoning and discovery. It's been a privilege to pursue this Perspective with @MengdiWang10 and an outstanding group of scientists and innovators advancing the intersection of computation, AI, science, and medicine. We're excited to continue exploring where this vision leads. Our preprint: preprints.org/manuscript/202… Preprint led by Jian and Aviv team: preprints.org/manuscript/202… This also kicks off a new Gladstone-Stanford AI Hub efforts, led by Katie Pollard at @GladstoneInst and myself, with an amazing team of scientists including Emma Lundberg (@Prof_Lundberg), Brian L Trippe (@brianltrippe ), Anshul Kundaje (@anshulkundaje ), Barbara Engelhardt (@BeEngelhardt), Christina Theodoris (@TheodorisLab), Catherine Tcheandjieu (@ines_catherine), Bruce Conklin, Alexander Marson, Stacie Dodgson (@StacieDodgson), Seth Shipman (@seth_shipman), Vijay Ramani, Danielle Swaney (@dlswaney), and Nevan Krogan. Excited to be building together across two great institutions!
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…
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…
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…
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