Prediction Methods Of Hot Region Based On Complex Networks And Communities Detection

Recently, hotspots are distributed in the protein interfaces, but not uniformly. Hotspotscluster closely is packed in the local area, known as hot regions. Hot region is the key factor tomaintain stability and coordination of protein-protein interactions (PPIs) and bring proteinsbiological function to play. The discovery of hot regions has important significance to revealprotein functional activities such as cell metabolism and signal transduction pathways, immunerecognition, DNA replication, gene translation and protein synthesis. The conformation andprediction of the hot regions effectively is a worth researching topics.This research is based on the complex network characteristics and community structures ofPPIs, combined with the community detection method and hotspots retrieving strategy to predicthot regions. We propose a prediction method of Hot Regions Prediction Based on Hotspots(HRPH), which is based on the hotspots result sets and the hot region formed rules to constructthe preliminary hot regions prediction. After obtaining the preliminary hot region prediction set,we detect the communities of hot regions by the community detection method, and use ahotspots retrieving strategy to correct and recover the false positive and false negative residuesof the preliminary results. Finally, we find the final hot regions through the optimization of thepreliminary results.The performance evaluation is based on6kinds of standards. We compare HRPH with theprevious prediction methods, which shows that the method proposed in this thesis improves theaccuracy of prediction and has higher reliability.