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.
Lo Bosco, G., Rizzo, R., Fiannaca, A., La Rosa, M., Urso, A. (2018). Variable Ranking Feature Selection for the Identification of Nucleosome Related Sequences. In A. Benczúr, B. Thalheim, T. Horváth, S. Chiusano, T. Cerquitelli, C. Sidló, et al. (a cura di), New Trends in Databases and Information Systems (pp. 314-324). Springer Verlag [10.1007/978-3-030-00063-9_30].
Variable Ranking Feature Selection for the Identification of Nucleosome Related Sequences
Lo Bosco, Giosué;Rizzo, Riccardo;Urso, Alfonso
2018-01-01
Abstract
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.File | Dimensione | Formato | |
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