Scientists investigate disease targets by studying gene expression data often obtained by assaying entire cell populations. For instance, researchers used bulk RNA sequencing to discover druggable cancer-associated protein targets1 and to uncover potential blood-based biomarkers for the early diagnosis of Alzheimer’s disease.2
More recently, scientists have turned to single cell RNA sequencing (scRNA-seq), which provides insights into how gene expression varies between individual cells.3 Scientists typically analyze scRNA-seq data using machine learning tools that have been built from scratch to carry out specific individual tasks.
Bo Wang, a computational biologist, and his team of computer scientists and cell biologists at the University of Toronto have built a new artificial intelligence (AI) model called single cell generative pretrained transformer, or scGPT, which can be finetuned to carry out a diverse range of tasks using scRNA-seq data. These tasks include predicting the effects of manipulating specific genes and merging distinct batches of ...
