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Protein Protein Interaction (PPI) can be considered as network. Alignment is the process of mapping nodes from one network to another network. The main objective of network alignment is to identify small, well defined interactome units such as protein complexes or conserved pathways that are analogous in the input network. Network alignment uncovers the relationship between protein complexes and functions. Similarity between two graph structures can be identified by evaluating the topology. Network alignment identifies either topological or sequential similarity. Gene annotations reveal the functional or sequential similarity and it can be evaluated based on semantic similarity. In this paper, we review the various network aligners and classify them according to the methodologies. We discuss the different evaluation metrics and the popular databases of protein interactions.

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Aim: A similarity evaluation measure for Gene Ontology GO terms is developed. Results:The proposed method takes into account the semantics hidden in ontologies or the term level information content, membership of term, and topology-based similarity measures. The proposed method is evaluated on positive and negative dataset of UniProt, Protein family clans and the Pearson's correlation with other existing methods. Conclusion:The experimental results exhibited a major supremacy of the proposed method over other semantic similarity measures.

Proteins interact each other to perform many cellular activities. These interactions can be considered as Protein Protein Interaction networks (PPI). Interacting proteins form protein complexes. Mapping nodes between networks is denoted as alignment. The main intention of network alignment approach is to identify the protein complexes, which in turn helps to identify the functionality of protein complexes in various cellular systems. These interactome units form the conserved pathways between the networks. So network alignment requires lot of attention and several algorithms and techniques have been proposed to address this. The study of PPI is widely recognized to know more about the underlying complex disease because proteins associated with any disease get connected and form subgraphs or pathways. In this paper, the authors compared the various aligners, the performance evaluation metrics, the common databases used for PPI evaluation and the importance of PPI network in biomedical research.