predicting β-turns in protein using kernel logistic regression
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2013
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Abstract
A β-turn is a secondary protein structure type that plays a significant role in protein configuration and function. On average 25% of amino acids in protein structures are
located in β-turns. It is very important to develope an accurate and efficient method for β-turns prediction. Most of the current successful β-turns prediction methods use support vector
machines (SVMs) or neural networks (NNs). The kernel logistic regression (KLR) is a powerful classification technique that has been applied successfully in many classification problems. However, it is often not found in β-turns classification, mainly because it is computationally expensive. In this paper, we used KLR to obtain sparse β-turns prediction in short evolution time. Secondary structure information and position-specific scoring matrices (PSSMs) are utilized as input features. We achieved Qtotal of 80.7% and MCC of 50% on BT426 dataset. These results show that KLR method with the right algorithm can yield
performance equivalent to or even better than NNs and SVMs in β-turns prediction. In addition, KLR yields probabilistic outcome and has a well-defined extension to multiclass case.
| Reference Key |
elbashir2013biomedpredicting
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|---|---|
| Authors | ;Murtada Khalafallah Elbashir;Yu Sheng;Jianxin Wang;FangXiang Wu;Min Li |
| Journal | spectrochimica acta - part a: molecular and biomolecular spectroscopy |
| Year | 2013 |
| DOI |
10.1155/2013/870372
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| URL | |
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