Predicting [beta]-turns in protein using kernel logistic regression.
A ß-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 ß-turns. It is very important to develope an accurate and efficient method for ß-turns prediction. Most of the curren...
| Publicado en: | BioMed Research International Vol. 2013; pp. 870372 - 870373 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104287164&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104287164 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2013 vid: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104287164 104287164 2012116757 NLM23509793 PMC3590576 104287164 ppf: 870372 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Predicting [beta]-turns in protein using kernel logistic regression. aug: au: Elbashir, Murtada Khalafallah Sheng, Yu Wang, Jianxin Wu, Fangxiang Li, Min affil: School of Information Science and Engineering, Central South University, Changsha 410083, China. sug: subj: Logistic Regression Proteins Bioinformatics Methods Resource Databases Neural Networks (Computer) Probability Reproducibility of Results Software Algorithms Human ab: A ß-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 ß-turns. It is very important to develope an accurate and efficient method for ß-turns prediction. Most of the current successful ß-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 ß-turns classification, mainly because it is computationally expensive. In this paper, we used KLR to obtain sparse ß-turns prediction in short evolution time. Secondary structure information and position-specific scoring matrices (PSSMs) are utilized as input features. We achieved Q total 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 ß-turns prediction. In addition, KLR yields probabilistic outcome and has a well-defined extension to multiclass case. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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