Deep learning sharpens views of cells and genes

From Nature

The research relied on a convolutional neural network, a type of deep-learning algorithm that is transforming how biologists analyse images. Scientists are using the approach to find mutations in genomes and predict variations in the layout of single cells. Google’s method, described in a preprint in August (R. Poplin et al. Preprint at; 2017), is part of a wave of new deep-learning applications that are making image processing easier and more versatile — and could even identify overlooked biological phenomena.

Cell biologists at the Allen Institute for Cell Science in Seattle, Washington, are using convolutional neural networks to convert flat, grey images of cells captured with light microscopes into 3D images in which some of a cell’s organelles are labelled in colour_. The approach eliminates the need to stain cells — a process that requires more time and a sophisticated_ lab, and can damage the cell. Last month, the group published details of an advanced technique that can predict the shape and location of even more cell parts using just a few pieces of data — such as the cell’s outline (G. R. Johnson et al. Preprint at bioRxiv; 2017).

Other machine-learning connoisseurs in biology have set their sights on new frontiers, now that convolutional neural networks are taking flight for image processing. “Imaging is important, but so is chemistry and molecular data,” says Alex Wolf, a computational biologist at the German Research Center for Environmental Health in Neuherberg. Wolf hopes to tweak neural networks so that they can analyse gene expression. “I think there will be a very big breakthrough in the next few years,” he says, “that allows biologists to apply neural networks much more broadly.”