Graph algorithms for predicting subcellular localization at the pathway level
This work addresses the dynamic nature of protein localization in disease and cellular processes for biologists, but it is incremental as it applies existing methods to a new context.
The authors tackled the problem of predicting protein subcellular localization dynamically within biological pathways by developing graph algorithms, achieving results through comparison of models like graph neural networks and probabilistic graphical models on curated databases and a case study with human fibroblasts under viral infection.
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.