Please use this identifier to cite or link to this item:
Title: Boosting the Detection of Transposable Elements Using Machine Learning
Authors: Loureiro,T
Rui Camacho
Nuno Fonseca
Issue Date: 2013
Abstract: 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.
metadata.dc.type: article
Appears in Collections:CRACS - Articles in International Journals

Files in This Item:
File Description SizeFormat 
P-008-C11.pdf157.78 kBAdobe PDFThumbnail

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.