Transfer learning enables predictions in network biology.

ABSTRACT: Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding^1,2 and computer vision³ 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 task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data

SUBMITTER: Theodoris CV 

PROVIDER: S-EPMC10949956 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature