Document Detail


Hierarchical Motif Vectors for Prediction of Functional Sites in Amino Acid Sequences Using Quasi-supervised Learning.
MedLine Citation:
PMID:  22585139     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
We propose hierarchical motif vectors to represent local amino acid sequence configurations for predicting the functional attributes of amino acid sites on a global scale in a quasi-supervised learning framework. The motif vectors are constructed via wavelet decomposition on the variations of physico-chemical amino acid properties along the sequences. We then formulate a prediction scheme for the functional attributes of amino acid sites in terms of the respective motif vectors using the quasi-supervised learning algorithm that carries out predictions for all sites in consideration using only the experimentally verified sites. We have carried out comparative performance evaluation of the proposed method on the prediction of N-glycosylation of 55184 sites possessing the consensus N-glycosylation sequon identified over 15104 human proteins, out of which only 1939 were experimentally verified N-glycosylation sites. In the experiments, the proposed method achieved better predictive performance than the alternative strategies from the literature. In addition, the predicted N-glycosylation sites showed good agreement with existing potential annotations, while the novel predictions belonged to proteins known to be modified by glycosylation.
Authors:
Bilge Karacali
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-5-8
Journal Detail:
Title:  IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM     Volume:  -     ISSN:  1557-9964     ISO Abbreviation:  -     Publication Date:  2012 May 
Date Detail:
Created Date:  2012-5-15     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101196755     Medline TA:  IEEE/ACM Trans Comput Biol Bioinform     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Izmir Institute of Technology, Urla.
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