Mapping Gene Expression to an Interpretable Semantic Space
Cell embeddings organize single-cell expression data, but their dimensions have no biological meaning, so clusters are interpreted afterward. We present MESIC (Mapping Expression to Semantic space with Interpretable Components), which builds the written knowledge about genes held in curated databases into the dimensions themselves. A biomedical language model converts each gene's summary into a…
We haven't written up this one. bioRxiv has the full story — the link below goes straight to it.
