Biological networks are used to describe molecular machineries within cells. Therefore, the comparison among networks of different species or different states (e.g., healthy vs diseased people) may reveal relevant knowledge such as information about evolution. Comparison among networks is usually performed using network alignment algorithms. In a previous work, we used global network alignment to produce prior knowledge about networks that has been used on local network alignment algorithm. Here, we present Simulated Annealing-Global Local Aligner (SL-GLAlign), a novel framework methodology based on the use of topological information extracted from global alignment to guide the building of local alignment. To assess our methodology, we tested SL-GLAlign on several biological networks. Comparing with the state-of-the-art local alignment algorithms, SL-GLAlign is able to improve the alignment building.
