Altmetric score 14.05 (top 7.2%)

Author: Tanel Pärnamaa
Research area: bioinformatics

Open peer-review

Review content is open, signing review is optional.

Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning

Created on 28th April 2016

Tanel Pärnamaa; Leopold Parts;

High throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently tagged protein resides, a task relatively simple for an experienced human, but difficult to automate on a computer. Here, we train an 11-layer neural network on data from mapping thousands of yeast proteins, achieving per cell localization classification accuracy of 91%, and per protein accuracy of 99% on held out images. We confirm that low-level network features correspond to basic image characteristics, while deeper layers separate localization classes. Using this network as a feature calculator, we train standard classifiers that assign proteins to previously unseen compartments after observing only a small number of training examples. Our results are the most accurate subcellular localization classifications to date, and demonstrate the usefulness of deep learning for high throughput microscopy.

Show more

Review Summary

This paper has 0 completed reviews and 0 reviews in progress.

# Status Date