Neural nets supplant marker genes in analyzing single cell RNA sequencing

(Carnegie Mellon University) Computer scientists at Carnegie Mellon University say neural networks and supervised machine learning techniques can efficiently characterize cells that have been studied using single cell RNA-sequencing (scRNA-seq). This finding, published in the online journal Nature Communications, could help researchers identify new cell subtypes and differentiate between healthy and diseased cells.
Source: EurekAlert! - Biology - Category: Biology Source Type: news