Deep learning architectures for DNA sequence classification

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.