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Z. Aung and K. L. Tan, “Automatic 3D Protein Structure Classification without Structural Alignment,” Jounal of Computational Biology, Vol. 12, No. 9, 2005, pp. 12211241. doi:10.1089/cmb.2005.12.1221

has been cited by the following article:

• TITLE: Consensus Decision for Protein Structure Classification

  AUTHORS: Khaddouja Boujenfa, Mohamed Limam

  KEYWORDS: Bioinformatics Databases; Classification; Mining Methods and Algorithms; Similarity Measures

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.4 No.3, August 30, 2012

  ABSTRACT: The fundamental aim of protein classification is to recognize the family of a given protein and determine its biological function. In the literature, the most common approaches are based on sequence or structure similarity comparisons. Other methods use evolutionary distances between proteins. In order to increase classification performance, this work proposes a novel method, namely Consensus, which combines the decisions of several sequence and structure comparison tools to classify a given structure. Additionally, Consensus uses the evolutionary information of the compared structures. Our method is tested on three databases and evaluated based on different criteria. Performance evaluation of our method shows that it outperforms the different classifiers used separately and gives higher classification perfor-mance than a free-alignment method, namely ProtClass.