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 affects 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.
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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: Benczúr, A., et al. New Trends in Databases and Information Systems. ADBIS 2018. Communications in Computer and Information Science, vol 909. Springer, Cham. https://doi.org/10.1007/978-3-030-00063-9_30
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