Algorithm | Bioinformatics | Cell | Computational Models | Deep Learning | Machine Learning | News | OMICS | Paper Summary | Tools
DGCyTOF: Deep Learning with Graphic Cluster Visualisation Model for Predicting Cell Types from Single-Cell Mass Cytometry Data
Researchers from The Ohio State University utilized deep-learning classification and hierarchical stable-clustering approaches to predict cell types from single-cell mass cytometry data. The DGCyTOF approach could facilitate the analysis of single-cell RNASeq data and other omics data. Single-cell mass cytometry, also called cytometry by time of flight (CyTOF), is a powerful high-throughput technology that permits…