StackTIS: A stacked generalization approach for effective prediction of translation initiation sites

Authors:
George Tzanis;Christos Berberidis;Ioannis Vlahavas
Affiliations:
Department of Informatics, Aristotle University of Thessaloniki, Greece;School of Science and Technology, International Hellenic University, Greece;Department of Informatics, Aristotle University of Thessaloniki, Greece
Venue:
Computers in Biology and Medicine
Year:
2012

Citing 12
Cited 0

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Is Combining Classifiers with Stacking Better than Selecting the Best One?

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Markov Encoding for Detecting Signals in Genomic Sequences

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Translation Initiation Sites Prediction with Mixture Gaussian Models in Human cDNA Sequences

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ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Issues in stacked generalization

Journal of Artificial Intelligence Research

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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.