Deep Learning Architectures for DNA Sequence Classification

Giosue' Lo Bosco, Mattia Antonino Di Gangi, Giosué Lo Bosco

Risultato della ricerca: Conference contribution

18 Citazioni (Scopus)

Abstract

DNA sequence classification is a key task in a generic computational framework for biomedical data analysis, and in recent years several machine learning technique have been adopted to successful accomplish with this task. Anyway, the main difficulty behind the problem remains the feature selection process. Sequences do not have explicit features, and the commonly used representations introduce the main drawback of the high dimensionality. For sure, machine learning method devoted to supervised classification tasks are strongly dependent on the feature extraction step, and in order to build a good representation it is necessary to recognize and measure meaningful details of the items to classify. Recently, neural deep learning architectures or deep learning models, were proved to be able to extract automatically useful features from input patterns. In this work we present two different deep learning architectures for the purpose of DNA sequence classification. Their comparison is carried out on a public data-set of DNA sequences, for five different classification tasks.
Lingua originaleEnglish
Titolo della pubblicazione ospiteFuzzy Logic and Soft Computing Applications, 11th International Workshop, WILF 2016, Naples, Italy, December 19–21, 2016, Revised Selected Papers
Pagine162-171
Numero di pagine10
Stato di pubblicazionePublished - 2016

Serie di pubblicazioni

NomeLECTURE NOTES IN ARTIFICIAL INTELLIGENCE

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
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