Document Detail


Predicting flexible length linear B-cell epitopes.
MedLine Citation:
PMID:  19642274     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Identifying B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting B-cell epitopes are highly desirable. We explore two machine learning approaches for predicting flexible length linear B-cell epitopes. The first approach utilizes four sequence kernels for determining a similarity score between any arbitrary pair of variable length sequences. The second approach utilizes four different methods of mapping a variable length sequence into a fixed length feature vector. Based on our empirical comparisons, we propose FBCPred, a novel method for predicting flexible length linear B-cell epitopes using the subsequence kernel. Our results demonstrate that FBCPred significantly outperforms all other classifiers evaluated in this study. An implementation of FBCPred and the datasets used in this study are publicly available through our linear B-cell epitope prediction server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/.
Authors:
Yasser El-Manzalawy; Drena Dobbs; Vasant Honavar
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Computational systems bioinformatics / Life Sciences Society. Computational Systems Bioinformatics Conference     Volume:  7     ISSN:  1752-7791     ISO Abbreviation:  Comput Syst Bioinformatics Conf     Publication Date:  2008  
Date Detail:
Created Date:  2009-07-31     Completed Date:  2009-08-28     Revised Date:  2014-09-22    
Medline Journal Info:
Nlm Unique ID:  101294517     Medline TA:  Comput Syst Bioinformatics Conf     Country:  United States    
Other Details:
Languages:  eng     Pagination:  121-32     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Amino Acid Sequence
Artificial Intelligence*
Computer Simulation
Epitope Mapping / methods*
Epitopes, B-Lymphocyte / chemistry*
Linear Models*
Models, Chemical*
Molecular Sequence Data
Pattern Recognition, Automated / methods*
Sequence Analysis, Protein / methods*
Grant Support
ID/Acronym/Agency:
GM066387/GM/NIGMS NIH HHS; R21 GM066387/GM/NIGMS NIH HHS; R33 GM066387/GM/NIGMS NIH HHS; R33 GM066387-04/GM/NIGMS NIH HHS
Chemical
Reg. No./Substance:
0/Epitopes, B-Lymphocyte
Comments/Corrections

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