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Efficiency and Effectiveness Metrics in Evolutionary Algorithms and Their Application Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Identification of Hot Regions in Protein Interfaces: Combining Density Clustering and Neighbor Residues Improves the Accuracy

Authors : Jing Hu, Xiaolong Zhang

Published in: Intelligent Computing Theories and Methodologies

Publisher: Springer International Publishing

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Abstract

Discovering hot regions in protein–protein interaction is important for understanding the interactions between proteins, while because of the complexity and time-consuming of experimental methods, the computational prediction method can be very helpful to improve the efficiency to predict hot regions. In hot region prediction research, some models are based on structure information, and others are based on a protein interaction network. However, the prediction accuracy of these methods can still be improved. In this paper, a new method that uses density-based incremental clustering to predict hot regions and optimizes the predicted hot regions using neighbor residues is proposed. Experimental results show that the proposed method significantly improves the prediction performance of hot regions.

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previous chapter Identification of Hot Regions in Protein-Protein Interactions Based on SVM and DBSCAN
next chapter Prediction of Protein Structural Classes Based on Predicted Secondary Structure
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Metadata
Title
Identification of Hot Regions in Protein Interfaces: Combining Density Clustering and Neighbor Residues Improves the Accuracy
Authors
Jing Hu
Xiaolong Zhang
Copyright Year
2015
Book
Intelligent Computing Theories and Methodologies

Print ISBN: 978-3-319-22185-4

Electronic ISBN: 978-3-319-22186-1

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-22186-1_39

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