Theodoris Lab

mapping gene networks to enable network-based therapeutic discovery in cardiovascular disease

[Theodoris Lab @TheodorisLab]

Happy to share a protocol for discovery of candidate therapeutic targets with Geneformer by @YujieZh1729!

Nature Protocols link: https://t.co/6cIvUThS1f
Google Colab: https://t.co/pqbXvI3cw4

[Theodoris Lab @TheodorisLab]

Really enjoyed talking with science journalist Andrew Han about art and science on his new podcast, Ion Genomics! Check it out here: https://t.co/2jR2PRDs92

[Theodoris Lab @TheodorisLab]

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

[Theodoris Lab @TheodorisLab]

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.

[Theodoris Lab @TheodorisLab]

Manuscript: https://t.co/zdLn785KYR
Press release: https://t.co/EPLgnJZh4K
Model: https://t.co/QJxXsHxOTs
Code: https://t.co/8I7wrAexNx
Blog post: https://t.co/quK7MJJjMU

[Theodoris Lab @TheodorisLab]

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

[Theodoris Lab @TheodorisLab]

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! https://t.co/ugXyeOsi2W

[Theodoris Lab @TheodorisLab]

Thank you to the whole team for the wonderful collaboration! Javier Gómez Ortega, @Sid_Mahesh_12, Tarak Nandi, @madduri, and @PelkaLab!

https://t.co/18d1eHlZxN

[Theodoris Lab @TheodorisLab]

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: https://t.co/prZmTVIq7z
Model: https://t.co/AsF5MIBhTV

[Theodoris Lab @TheodorisLab]

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: https://t.co/prZmTVIq7z
Model: https://t.co/AsF5MIBhTV

[Theodoris Lab @TheodorisLab]

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.