Abstract
The increased availability of large-scale protein-protein interaction (PPI) data has made it possible to have a network level understanding of the basic components and organization of the cell machinery. A significant number of proteins in protein interaction networks (PIN) remain uncharacterized and predicting their function remains a major challenge. We propose a novel distance metric for PIN clustering. First we augment the graph representing the PIN with weights derived from Gene Ontology (GO) semantic similarity and we use this augmented representation in a random walk with restarts (RWR) process. The distance between a pair of proteins is calculated from the steady state distribution of the RWR. We validate our approach by function prediction via clustering in a purified and reliable Saccharomyces cerevisiae PIN. We show that the rise of function prediction performance when using the novel distance metric is significant, as compared to traditional approaches.
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Trivodaliev, K., Ivanoska, I., Kalajdziski, S., Kocarev, L. (2015). Novel Gene Ontology Based Distance Metric for Function Prediction via Clustering in Protein Interaction Networks. In: Bogdanova, A., Gjorgjevikj, D. (eds) ICT Innovations 2014. ICT Innovations 2014. Advances in Intelligent Systems and Computing, vol 311. Springer, Cham. https://doi.org/10.1007/978-3-319-09879-1_17
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DOI: https://doi.org/10.1007/978-3-319-09879-1_17
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