Graph algorithms for predicting subcellular localization at the pathway level

Autor:	Magnano CS; Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA.; Morgridge Institute for Research, Madison, WI, USA; Center for Computational Biomedicine, Harvard Medical School, Boston, MA, USA, Gitter A; Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA.; Morgridge Institute for Research, Madison, WI, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
Jazyk:	angličtina
Zdroj:	Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing [Pac Symp Biocomput] 2023; Vol. 28, pp. 145-156.
DOI:	10.1142/9789811270611_0014
Abstrakt:	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.
Databáze:	MEDLINE
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