Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Fast condensed nearest neighbor rule
ICML '05 Proceedings of the 22nd international conference on Machine learning
A grid-based architecture for nearest neighbor based condensation of huge datasets
UPGRADE '08 Proceedings of the third international workshop on Use of P2P, grid and agents for the development of content networks
BioTRON: a biological workflow management system
Proceedings of the 2011 ACM Symposium on Applied Computing
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Many proteins are composed of two or more subunits, each associated with different polypeptide chains. The number and the arrangement of subunits forming a protein are referred to as quaternary structure. The quaternary structure of a protein is important, since it characterizes the biological function of the protein when it is involved in specific biological processes. Unfortunately, quaternary structures are not trivially deducible from protein amino acid sequences. In this work, we propose a protein quaternary structure classification method exploiting the functional domain composition of proteins. It is based on a nearest neighbor condensation technique in order to reduce both the portion of dataset to be stored and the number of comparisons to carry out. Our approach seems to be promising, in that it guarantees an high classification accuracy, even though it does not require the entire dataset to be analyzed. Indeed, experimental evaluations show that the method here proposed selects a small dataset portion for the classification (of the order of the 6.43%) and that it is very accurate (97.74%).