Automated Protein Classification Using Consensus Decision

We propose a novel technique for automatically generating the SCOP classification of a protein structure with high accuracy. High accuracy is achieved by combining the decisions of multiple methods using the consensus of a committee (or an ensemble) classifier. Our technique is rooted in machine learning which shows that by judicially employing component classifiers, an ensemble classifier can be constructed to outperform its components. We use two sequence- and three structure-comparison tools as component classifiers. Given a protein structure, using the joint hypothesis, we first determine if the protein belongs to an existing category (family, superfamily, fold) in the SCOP hierarchy. For the proteins that are predicted as members of the existing categories, we compute their family-, superfamily-, and fold-level classifications using the consensus classifier. We show that we can significantly improve the classification accuracy compared to the individual component classifiers. In ...

• Type: Conference
• Year: 2004
• Where: CSB
• Authors: Tolga Can, Orhan Çamoglu, Ambuj K. Singh, Yuan-Fang Wang
• Keywords: Bioinformatics, Component Classifiers, CSB 2004, Protein Structure, SCOP Classification