Reconstructing cell cycle and disease progression using deep learning - Philipp Eulenberg (Nature Portfolio, 2017)
The interpretation of information-rich, high-throughput single-cell data is a challenge requiring sophisticated computational tools. Here the authors demonstrate a deep convolutional neural network that can classify cell cycle status on-the-fly.