Human-level protein localization with con-volutional neural networks

Elisabeth Rumetshofer, Markus Hofmarcher, Clemens Röhrl, Sepp Hochreiter, Günter Klambauer

Research output: Contribution to conferencePaperpeer-review

6 Citations (Scopus)

Abstract

Localizing a specific protein in a human cell is essential for understanding cellular functions and biological processes of underlying diseases. A promising, low-cost, and time-efficient biotechnology for localizing proteins is high-throughput fluorescence microscopy imaging (HTI). This imaging technique stains the protein of interest in a cell with fluorescent antibodies and subsequently takes a microscopic image. Together with images of other stained proteins or cell organelles and the annotation by the Human Protein Atlas project, these images provide a rich source of information on the protein location which can be utilized by computational methods. It is yet unclear how precise such methods are and whether they can compete with human experts. We here focus on deep learning image analysis methods and, in particular, on Convolutional Neural Networks (CNNs) since they showed overwhelming success across different imaging tasks. We propose a novel CNN architecture “GapNet-PL” that has been designed to tackle the characteristics of HTI data and uses global averages of filters at different abstraction levels. We present the largest comparison of CNN architectures including GapNet-PL for protein localization in HTI images of human cells. GapNet-PL outperforms all other competing methods and reaches close to perfect localization in all 13 tasks with an average AUC of 98% and F1 score of 78%. On a separate test set the performance of GapNet-PL was compared with three human experts and 25 scholars. GapNet-PL achieved an accuracy of 91%, significantly (p-value 1.1e6) outperforming the best human expert with an accuracy of 72%.1

Original languageEnglish
Publication statusPublished - 2019
Externally publishedYes
Event7th International Conference on Learning Representations, ICLR 2019 - New Orleans, United States
Duration: 6 May 20199 May 2019

Conference

Conference7th International Conference on Learning Representations, ICLR 2019
CountryUnited States
CityNew Orleans
Period06.05.201909.05.2019

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