Neural Nets Supplant Marker Genes in Analyzing Single Cell RNA SequencingComputer 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 could help researchers identify new cellsubtypes and differentiate between healthy and diseased cells.
CPCB’s Biggest Incoming Class Starts Their First Day of ClassesA total of 21 students have joined the program
Dr. Anne-Ruxandra Carvunis named 2018 SEARLE ScholarWednesday, May 16, 2018
Dr. Anne-Ruxandra Carvunis named 2018 SEARLE ScholarDr. Anne-Ruxandra Carvunis named 2018 SEARLE Scholar
New Carnegie Mellon Dynamic Statistical Model Follows Gene Expressions Over TimeTuesday, February 06, 2018
New Carnegie Mellon Dynamic Statistical Model Follows Gene Expressions Over TimeResearchers at Carnegie Mellon University have developed a new dynamic statistical model to visualize changing patterns in networks, including gene expression during developmental periods of the brain.
First Year PhD student has first-authored paper acceptedThursday, January 11, 2018