2-AIN-505, 2-AIN-251: Seminár z bioinformatiky (1) a (3)
Zima 2017

Tanel Parnamaa, Leopold Parts. Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning. G3 (Bethesda), 7(5):1385-1392. 2017.

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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.