Document Detail


StackTIS: A stacked generalization approach for effective prediction of translation initiation sites.
MedLine Citation:
PMID:  22079568     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
The prediction of the translation initiation site in an mRNA or cDNA sequence is an essential step in gene prediction and an open research problem in bioinformatics. Although recent approaches perform well, more effective and reliable methodologies are solicited. We developed an adaptable data mining method, called StackTIS, which is modular and consists of three prediction components that are combined into a meta-classification system, using stacked generalization, in a highly effective framework. We performed extensive experiments on sequences of two diverse eukaryotic organisms (Homo sapiens and Oryza sativa), indicating that StackTIS achieves statistically significant improvement in performance.
Authors:
George Tzanis; Christos Berberidis; Ioannis Vlahavas
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-11-11
Journal Detail:
Title:  Computers in biology and medicine     Volume:  -     ISSN:  1879-0534     ISO Abbreviation:  -     Publication Date:  2011 Nov 
Date Detail:
Created Date:  2011-11-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  1250250     Medline TA:  Comput Biol Med     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2011 Elsevier Ltd. All rights reserved.
Affiliation:
Department of Informatics, Aristotle University of Thessaloniki, Greece.
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