Surface protein imputation from single cell transcriptomes by deep neural networks
preprint
OA: closed
Abstract
While single cell RNA sequencing (scRNA-seq) is invaluable for studying cell populations, cell-surface proteins are often integral markers of cellular function and serve as primary targets for therapeutic intervention. Here we propose a transfer learning framework, single c ell T ranscriptome to P rotein prediction with deep neural net work (cTP-net), to impute surface protein abundances from scRNA-seq data by learning from existing single-cell multi-omic resources.
My notes (saved in your browser only)
Citation neighborhood (no data yet)
We don't have any in-corpus citations linked to this paper yet. The paper's references may be in our DB but unresolved to ``paper_id`` (resolution happens at ingest when the cited DOI matches a row we already have). Run the cross-source citation reconcile pass to retry.
Source provenance
- europepmc
- last seen: 2026-05-19T01:45:01.086888+00:00