| Title: |
StackTIS: A Stacked Generalization Approach for Effective Prediction of Translation Initiation Sites |
| Author(s): |
G. Tzanis, C. Berberidis, I. Vlahavas.
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| Availability: |
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| Keywords: |
Translation initiation, Bioinformatics, Data mining, Machine learning, Classification, Stacking.
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| Appeared in: |
Computers in Biology and Medicine, Elsevier, Vol. 42, No. 1, pp. 61-69, 2012.
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| Abstract: |
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. |
| See also : |
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This paper has been cited by the following:
| 1 |
de Andrés, J., Lorca, P., Sánchez-Lasheras, F., de Cos-Juez, F.J., ‘Bankruptcy prediction and credit scoring: A review of recent developments based on hybrid systems and some related patents”, Recent Patents on Computer Science, 5 (1), pp. 11-20, 2012. |
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