  Protein subcellular localization is an important factor in normal cellular
processes and disease. While many protein localization resources treat it as
static, protein localization is dynamic and heavily influenced by biological
context. Biological pathways are graphs that represent a specific biological
context and can be inferred from large-scale data. We develop graph algorithms
to predict the localization of all interactions in a biological pathway as an
edge-labeling task. We compare a variety of models including graph neural
networks, probabilistic graphical models, and discriminative classifiers for
predicting localization annotations from curated pathway databases. We also
perform a case study where we construct biological pathways and predict
localizations of human fibroblasts undergoing viral infection. Pathway
localization prediction is a promising approach for integrating publicly
available localization data into the analysis of large-scale biological data.
