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BMC Research Notes

, 1:51

First Online: 23 July 2008Received: 14 June 2008Accepted: 23 July 2008DOI: 10.1186-1756-0500-1-51

Cite this article as: Chang, D.TH., Ou, YY., Hung, HG. et al. BMC Res Notes 2008 1: 51. doi:10.1186-1756-0500-1-51


BackgroundThough prediction of protein secondary structures has been an active research issue in bioinformatics for quite a few years and many approaches have been proposed, a new challenge emerges as the sizes of contemporary protein structure databases continue to grow rapidly. The new challenge concerns how we can effectively exploit all the information implicitly deposited in the protein structure databases and deliver ever-improving prediction accuracy as the databases expand rapidly.

FindingsThe new challenge is addressed in this article by proposing a predictor designed with a novel kernel density estimation algorithm. One main distinctive feature of the kernel density estimation based approach is that the average execution time taken by the training process is in the order of Onlogn, where n is the number of instances in the training dataset. In the experiments reported in this article, the proposed predictor delivered an average Q3 three-state prediction accuracy score of 80.3% and an average SOV segment overlap score of 76.9% for a set of 27 benchmark protein chains extracted from the EVA server that are longer than 100 residues.

ConclusionThe experimental results reported in this article reveal that we can continue to achieve higher prediction accuracy of protein secondary structures by effectively exploiting the structural information deposited in fast-growing protein structure databases. In this respect, the kernel density estimation based approach enjoys a distinctive advantage with its low time complexity for carrying out the training process.

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Autor: Darby Tien-Hao Chang - Yu-Yen Ou - Hao-Geng Hung - Meng-Han Yang - Chien-Yu Chen - Yen-Jen Oyang


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