Graph algorithms for predicting subcellular localization at the pathway level.

Magnano CS(1)(2)(3), Gitter A(1)(2)(4).

Author information:
(1)Department of Computer Sciences, University of Wisconsin-Madison, Madison,
WI, USA.
(2)Morgridge Institute for Research, Madison, WI, USA
(3)Center for Computational Biomedicine, Harvard Medical School, Boston, MA, USA
(4)Department of Biostatistics and Medical Informatics, University of
Wisconsin-Madison, Madison, WI, USA

Pac Symp Biocomput. 2023;28:145-156

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Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.

Abstract

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.