| Analysis and identification of beta-turn types using multinomial logistic regression and artificial neural network. | |
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MedLine Citation:
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PMID: 17599929 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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MOTIVATION: So far various statistical and machine learning techniques applied for prediction of beta-turns. The majority of these techniques have been only focused on the prediction of beta-turn location in proteins. We developed a hybrid approach for analysis and prediction of different types of beta-turn. RESULTS: A two-stage hybrid model developed to predict the beta-turn Types I, II, IV and VIII. Multinomial logistic regression was initially used for the first time to select significant parameters in prediction of beta-turn types using a self-consistency test procedure. The extracted parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in beta-turn sequence. The most significant parameters were then selected using multinomial logistic regression model. Among these, the occurrences of glutamine, histidine, glutamic acid and arginine, respectively, in positions i, i + 1, i + 2 and i + 3 of beta-turn sequence had an overall relationship with five beta-turn types. A neural network model was then constructed and fed by the parameters selected by multinomial logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains by 9-fold cross-validation. It has been observed that the hybrid model gives a Matthews correlation coefficient (MCC) of 0.235, 0.473, 0.103 and 0.124, respectively, for beta-turn Types I, II, IV and VIII. Our model also distinguished the different types of beta-turn in the embedded binary logit comparisons which have not carried out so far. AVAILABILITY: Available on request from the authors. |
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Authors:
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Mehdi Poursheikhali Asgary; Samad Jahandideh; Parviz Abdolmaleki; Anoshirvan Kazemnejad |
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Publication Detail:
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Type: Journal Article Date: 2007-06-28 |
Journal Detail:
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Title: Bioinformatics (Oxford, England) Volume: 23 ISSN: 1367-4811 ISO Abbreviation: Bioinformatics Publication Date: 2007 Dec |
Date Detail:
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Created Date: 2007-11-28 Completed Date: 2007-12-21 Revised Date: 2009-11-04 |
Medline Journal Info:
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Nlm Unique ID: 9808944 Medline TA: Bioinformatics Country: England |
Other Details:
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Languages: eng Pagination: 3125-30 Citation Subset: IM |
Affiliation:
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Department of Biophysics, Faculty of Basic Sciences, Tarbiat Modares University, Tehran, Iran. |
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Amino Acid Sequence Computer Simulation Logistic Models Models, Chemical* Models, Molecular* Molecular Sequence Data Multivariate Analysis Neural Networks (Computer)* Pattern Recognition, Automated / methods* Protein Conformation Regression Analysis Sequence Analysis, Protein / methods* |
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