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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Boosting the Detection of Transposable Elements Using Machine Learning
verfasst von : Tiago Loureiro, Rui Camacho, Jorge Vieira, Nuno A. Fonseca
Erschienen in: 7th International Conference on Practical Applications of Computational Biology & Bioinformatics
Verlag: Springer International Publishing
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Transposable Elements (TE) are sequences of DNA that move and transpose within a genome. TEs, as mutation agents, are quite important for their role in both genome alteration diseases and on species evolution. Several tools have been developed to discover and annotate TEs but no single one achieves good results on all different types of TEs. In this paper we evaluate the performance of several TEs detection and annotation tools and investigate if Machine Learning techniques can be used to improve their overall detection accuracy. The results of an
in silico
evaluation of TEs detection and annotation tools indicate that their performance can be improved by using machine learning classifiers.
