# New GO-based measures in multiple network alignment

## TL;DR

This paper introduces new methods to assess the quality of multiple protein interaction network alignments using Gene Ontology data.

## Contribution

Two novel GO-based measures are proposed that correlate with alignment quality and predict novel annotations.

## Key findings

- The new measures correlate with objective indicators like common orthologs.
- They strongly correlate with the ability to predict novel GO annotations.
- The measures outperform existing GO-based methods in this regard.

## Abstract

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.

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.

The scripts and the links to the raw and alignment data can be accessed at https://github.com/kimiayazdani/GO_Measures.git