Variable Ranking Feature Selection for the Identification of Nucleosome Related Sequences

Giosue' Lo Bosco, Massimo La Rosa, Antonino Fiannaca, Giosué Lo Bosco, Alfonso Urso, Riccardo Rizzo, Alfonso Urso, Riccardo Rizzo

Risultato della ricerca: Conference contribution

1 Citazioni (Scopus)


Several recent works have shown that K-mer sequence representation of a DNA sequence can be used for classification or identification of nucleosome positioning related sequences. This representation can be computationally expensive when k grows, making the complexity in spaces of exponential dimension. This issue effects significantly the classification task computed by a general machine learning algorithm used for the purpose of sequence classification. In this paper, we investigate the advantage offered by the so-called Variable Ranking Feature Selection method to select the most informative k − mers associated to a set of DNA sequences, for the final purpose of nucleosome/linker classification by a deep learning network. Results computed on three public datasets show the effectiveness of the adopted feature selection method.
Lingua originaleEnglish
Titolo della pubblicazione ospiteNew Trends in Databases and Information Systems
Numero di pagine11
Stato di pubblicazionePublished - 2018

Serie di pubblicazioni


All Science Journal Classification (ASJC) codes

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  • ???subjectarea.asjc.2600.2600???


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