Boosting the Detection of Transposable Elements Using Machine Learning

• Tipo: Artigo em Revista Científica Internacional
• Data: 2013

Autores

Loureiro, T (Autor) Outra; Rui Camacho (Autor) FEUP; Vieira, J (Autor) FEUP; Nuno A Fonseca (Autor) Outra

Indexação

Scopus - 10 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica