New GO-based measures in multiple network alignment

Author:

Yazdani Kimia1ORCID,Mousapour Reza2,Hayes Wayne B1ORCID

Affiliation:

1. Department of Computer Science, University of California , Irvine, CA 92697-3435, United States

2. Department of Computer Engineering, Sharif University of Technology , Tehran 1458889694, Iran

Abstract

Abstract Motivation Protein–protein interaction (PPI) networks provide valuable insights into the function of biological systems. Aligning multiple PPI networks may expose relationships beyond those observable by pairwise comparisons. However, assessing the biological quality of multiple network alignments is a challenging problem. Results We propose two new measures to evaluate the quality of multiple network alignments using functional information from Gene Ontology (GO) terms. When aligning multiple real PPI networks across species, we observe that both measures are highly correlated with objective quality indicators, such as common orthologs. Additionally, our measures strongly correlate with an alignment’s ability to predict novel GO annotations, which is a unique advantage over existing GO-based measures. Availability and implementation The scripts and the links to the raw and alignment data can be accessed at https://github.com/kimiayazdani/GO_Measures.git

Publisher

Oxford University Press (OUP)

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