http://www.cnr.it/ontology/cnr/individuo/prodotto/ID287887
Classification of selectively constrained DNA elements using feature vectors and rule-based classifiers (Articolo in rivista)
- Type
- Label
- Classification of selectively constrained DNA elements using feature vectors and rule-based classifiers (Articolo in rivista) (literal)
- Anno
- 2014-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1016/j.ygeno.2014.07.004 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Polychronopoulos D.; Weitschek E.; Dimitrieva S.; Bucher P.; Felici G.; Almirantis Y. (literal)
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- http://www.scopus.com/inward/record.url?eid=2-s2.0-84906046483&partnerID=q2rCbXpz (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
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- Institute of Biosciences and Applications, National Center for Scientific Research Demokritos, 15310 Athens, Greece; Department of Biochemistry and Molecular Biology, Faculty of Biology, National and Kapodistrian University of Athens, 15701 Athens, Greece; Department of Engineering, Roma Tre University, Via della Vasca Navale 79, 00146 Rome, Italy; Inst. of Systems Analysis and Computer Science Antonio Ruberti, National Research Council, Viale Manzoni 30, 00185 Rome, Italy; Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), CH-1015 Lausanne, Switzerland (literal)
- Titolo
- Classification of selectively constrained DNA elements using feature vectors and rule-based classifiers (literal)
- Abstract
- Scarce work has been done in the analysis of the composition of conserved non-coding elements (CNEs) that are identified by comparisons of two or more genomes and are found to exist in all metazoan genomes.Here we present the analysis of CNEs with a methodology that takes into account word occurrence at various lengths scales in the form of feature vector representation and rule based classifiers. We implement our approach on both protein-coding exons and CNEs, originating from human, insect ( Drosophila melanogaster) and worm (Caenorhabditis elegans) genomes, that are either identified in the present study or obtained from the literature.Alignment free feature vector representation of sequences combined with rule-based classification methods leads to successful classification of the different CNEs classes. Biologically meaningful results are derived by comparison with the genomic signatures approach, and classification rates for a variety of functional elements of the genomes along with surrogates are presented. © 2014 Elsevier Inc. (literal)
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