Announcements of Opportunity

Mapping the gene regulatory networks driving human disease enables the design of network-correcting treatments that target the core disease mechanism rather than merely managing symptoms. However, computationally inferring the network map requires large amounts of transcriptomic data to learn the connections between genes, which impedes network-correcting drug discovery in settings with limited data including rare disease and disease affecting clinically inaccessible tissues. Although data remains limited in these settings, recent advances in sequencing technologies have driven a rapid expansion in the amount of transcriptomic data available from human tissues more broadly. Recently, the concept of transfer learning has revolutionized fields such as natural language understanding and computer vision by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited application-specific data that would be too limited to yield meaningful predictions in isolation. To test whether an analogous approach could enable gene network predictions with limited data, we developed and pretrained our novel deep learning model, Geneformer, with a large-scale pretraining corpus we assembled with ~30 million human single cell transcriptomes, thereby generating an invaluable checkpoint from which fine-tuning towards a broad range of downstream applications could be pursued to accelerate discovery of key network regulators and candidate network-correcting therapies. Geneformer consistently boosted predictive accuracy in a diverse panel of downstream tasks using just a limited set of task-specific training examples. We will now leverage Geneformer’s learned understanding of contextual gene network dynamics to map the dysregulated gene network and discover candidate network-correcting therapeutics for hypertrophic cardiomyopathy, a prototypical rare disease affecting clinically inaccessible tissue where progress has been impeded by limited data.

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