Quantized multi-task learning for context-specific representations of gene network dynamics.

ABSTRACT: While often represented as static entities, gene networks are highly context-dependent. Here, we developed a multi-task learning strategy to yield context-specific representations of gene network dynamics. We assembled a corpus comprising ~103 million human single-cell transcriptomes from a broad range of tissues and diseases and performed a two stage pretraining, first with non-malignant cells to generate a foundational model and then with continual learning on cancer cells to tune the model to the cancer domain. We performed multi-task learning with the foundational model to learn context-specific representations of a broad range of cell types, tissues, developmental stages, and diseases. We then leveraged the cancer-tuned model to jointly learn cell states and predict tumor-restricting

SUBMITTER: Chen H 

PROVIDER: S-EPMC11370383 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature