Survey of biological network alignment: crossspecies analysis of conserved systems Comparative analysis of PPI networks between species is a significant step for predicting evolutionary conserved components or substructures in a system level. Network alignment is a computational technique to map similar components between networks. Aligning genomewide PPI networks maps functionally similar nodes and edges and identifies conserved interactions or conserved modules. Existing network alignment algorithms can be categorized into two groups: global and local network alignment. Global network alignment algorithms search for the best alignment of entire networks, whereas local network alignment algorithms produce the aligned pairs of small subnetworks with the highest scores. In this survey, we summarize prominent network alignment algorithms in both categories. We also present the methods to evaluate network alignment algorithms. When we compare the evaluation results of selected network alignment algorithms, PrimAlign outperformed the other global network alignment algorithms. Among local network alignment algorithms, LePrimAlign was superior to the competitors. We conclude with a future direction to expand the applicability of network alignment techniques.

Visualizing multifunctional PPI network with Gene Ontology annotation Proteinprotein interaction (PPI) network data contain interactions between two proteins in organisms. Some proteins are involved in more than one biological process. Those multifunctional proteins are important in the biological system because they connect various biological pathways. However, the multifunctionality of proteins increases the complexity of the PPI network and makes visualizing a challenging problem. In this research, we suggest a new method to visualize the multifunctional PPI network by integrating interaction data and Gene Ontology (GO) terms of each protein. By matching GO annotations of two proteins, we annotate edges between those two proteins with common GO terms. Also, we suggest a novel algorithm for color mapping specialized to express hierarchical relationships among GO terms based on the multidimensional scaling algorithm. Annotated edges are divided corresponding to their GO terms, and visualized by selected color, width, and opacity to represent semantic relationships among edges clearly. Those visual features are assigned to represent semantic distance among GO terms and their concreteness. This visualization method is available through the web.