Protein function is inherently linked to its localization within the cell,
and fluorescent microscopy data is an indispensable resource for learning
representations of proteins. Despite major developments in molecular
representation learning, extracting functional information from biological
images remains a non-trivial computational task. Current state-of-the-art
approaches use autoencoder models to learn high-quality features by
reconstructing images. However, such methods are prone to capturing noise and
imaging artifacts. In this work, we revisit deep learning models used for
classifying major subcellular localizations, and evaluate representations
extracted from their final layers. We show that simple convolutional networks
trained on localization classification can learn protein representations that
encapsulate diverse functional information, and significantly outperform
autoencoder-based models. We also propose a robust evaluation strategy to
assess quality of protein representations across different scales of biological
function.

本文发现了卷积网络可以学习蛋白质表征并且比自编码器模型表现更好。同时，提出了一种评估蛋白质表征质量的策略，并且关注蛋白质在细胞中的定位和荧光显微镜数据。