Transfer learning enables predictions in network biology.

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 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 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 in network biology.

AAV-Mediated Gene Augmentation Therapy Restores Critical Functions in Mutant PRPF31 iPSC-Derived RPE Cells.

Retinitis pigmentosa (RP) is the most common form of inherited vision loss and is characterized by degeneration of retinal photoreceptor cells and the retinal pigment epithelium (RPE). Mutations in pre-mRNA processing factor 31 () cause dominant RP via haploinsufficiency with incomplete penetrance. There is good evidence that the diverse severity of this disease is a result of differing levels of expression of the wild-type allele among patients.