Prediction of protein secondary structures with a novel kernel density estimation based classifier.
BACKGROUND
Though 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.


RESULTS
The 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 O(nlogn), 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.


CONCLUSIONS
The 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.
Chang
Darby Tien-Hao
DT
Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan, ROC. darby@ee.ncku.edu.tw
Ou
Yu-Yen
YY
Hung
Hao-Geng
HG
Yang
Meng-Han
MH
Chen
Chien-Yu
CY
Oyang
Yen-Jen
YJ