Adaptive Neural Network-Based Clustering of Yeast Protein–Protein Interactions

• Published in: Intelligent Information Technology pp. 49 - 57
• Main Authors: Eom, Jae-Hong, Zhang, Byoung-Tak
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 01.01.2004; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Cluster Distance; Cluster Model; Computer science; control theory; systems; Exact sciences and technology; Feature Selection; Learning and adaptive systems; Protein Interaction; Yeast Protein; Cluster analysis; Filter; Classification; K means algorithm; Neural network; Bioinformatics; Artificial intelligence; Adaptive method; Protein; Information theory
• ISBN: 9783540241263; 3540241264
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-540-30561-3_6

In this paper, we presents an adaptive neural network based clustering method to group protein–protein interaction data according to their functional categories for new protein interaction prediction in conjunction with information theory based feature selection. Our technique for grouping protein interaction is based on ART-1 neural network. The cluster prototype constructed with existing protein interaction data is used to predict the class of new protein interactions. The protein interaction data of S.cerevisiae (bakers yeast) from MIPS and SGD are used. The clustering performance was compared with traditional k-means clustering method in terms of cluster distance. According to the experimental results, the proposed method shows about 89.7% clustering accuracy and the feature selection filter boosted overall performances about 14.8%. Also, inter-cluster distances of cluster constructed with ART-1 based clustering method have shown high cluster quality.