Boosting the Detection of Transposable Elements Using Machine Learning

• Type: Article in International Scientific Journal
• Date: 2013

Authors

Loureiro, T (Author) Other; Rui Camacho (Author) FEUP; Vieira, J (Author) FEUP; Nuno A Fonseca (Author) Other

Indexing

Scopus - 10 Citations

Other information

Authenticus ID: P-008-C11

DOI: 10.1007/978-3-319-00578-2_12

Abstract (EN): 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. © Springer International Publishing Switzerland 2013.

Language: English

Type (Professor's evaluation): Scientific