کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
505315 | 864491 | 2012 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
StackTIS: A stacked generalization approach for effective prediction of translation initiation sites
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Biology and Medicine - Volume 42, Issue 1, January 2012, Pages 61–69
Journal: Computers in Biology and Medicine - Volume 42, Issue 1, January 2012, Pages 61–69
نویسندگان
George Tzanis, Christos Berberidis, Ioannis Vlahavas,