Salta al contenuto principale
Passa alla visualizzazione normale.


Variable Ranking Feature Selection for the Identification of Nucleosome Related Sequences

  • Autori: Lo Bosco, Giosué; Rizzo, Riccardo; Fiannaca, Antonino; La Rosa, Massimo; Urso, Alfonso
  • Anno di pubblicazione: 2018
  • Tipologia: Contributo in atti di convegno pubblicato in volume
  • OA Link:


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.