Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloDeep learning architectures for prediction of nucleosome positioning from sequences data
Anno di pubblicazione2018
Autore/iDi Gangi M.; Lo Bosco G. and Rizzo R.
Affiliazioni autoriFondazione Bruno Kessler (FBK) of Trento, Italy, University of Palermo, Italy, CNR-ICAR, Palermo, Italy
Autori CNR e affiliazioni
  • inglese
AbstractBackground Nucleosomes are DNA-histone complex, each wrapping about 150 pairs of double-stranded DNA. Their function is fundamental for one of the primary functions of Chromatin i.e. packing the DNA into the nucleus of the Eukaryote cells. Several biological studies have shown that the nucleosome positioning influences the regulation of cell type-specific gene activities. Moreover, computational studies have shown evidence of sequence specificity concerning the DNA fragment wrapped into nucleosomes, clearly underlined by the organization of particular DNA substrings. As the main consequence, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using a sequence features representation. Results In this work, we propose a deep learning model for nucleosome identification. Our model stacks convolutional layers and Long Short-term Memories to automatically extract features from short- and long-range dependencies in a sequence. Using this model we are able to avoid the feature extraction and selection steps while improving the classification performances. Conclusions Results computed on eleven data sets of five different organisms, from Yeast to Human, show the superiority of the proposed method with respect to the state of the art recently presented in the literature.
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RivistaBMC bioinformatics
Attiva dal 2000
Editore: BioMed Central, - [London]
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 1471-2105
Titolo chiave: BMC bioinformatics
Titolo proprio: BMC bioinformatics
Titolo abbreviato: BMC bioinformatics
Titoli alternativi:
  • BioMed Central bioinformatics
  • Bioinformatics
Numero volume della rivista19
Fascicolo della rivista418
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:WOS:000454362600010)
  • Scopus (Codice:2-s2.0-85053544966)
Parole chiaveNucleosome classification, Epigenetic, Deep learning networks, Recurrent neural networks
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Strutture CNR
  • ICAR — Istituto di calcolo e reti ad alte prestazioni
Moduli/Attività/Sottoprogetti CNR
Progetti Europei-
Deep learning architectures for prediction of nucleosome positioning from sequences data (documento privato )
Tipo documento: application/pdf