Deep learning algorithms for scRNAseq analysis have yielded positive results, but there are still more promising ways that need to be developed for regenerative medicine

Abstract

A detailed survey of the use of many deep learning algorithms on scRNAseq data for regenerative medicine was published in this article. Currently, the best deep learning algorithms for scRNAseq analysis have yielded positive results, but there are still more promising ways that need to be developed to better handle technical noise, account for cell expression variability, identify MSCs, and anticipate stem cell type. To gain access to scRNAseq data, these deep learning techniques need to be paired with scRNAseq data. The ability to identify cell types and functions accurately and fast utilizing these algorithms has not yet been made possible. In the study we conducted, we reached the conclusion that further research has to be done into how to apply deep learning algorithms to interpret scRNAseq data, which may be used to better cell therapy and regenerative medicine efforts.