DNA Sequence Classification by Convolutional Neural Network

Abstract

In recent years, a deep learning model called convolutional neural network with an ability of ex-tracting features of high-level abstraction from minimum preprocessing data has been widely used. In this research, we proposed a new approach in classifying DNA sequences using the con-volutional neural network while considering these sequences as text data. We used one-hot vec-tors to represent sequences as input to the model; therefore, it conserves the essential position information of each nucleotide in sequences. Using 12 DNA sequence datasets, we evaluated our proposed model and achieved significant improvements in all of these datasets. This result has shown a potential of using convolutional neural network for DNA sequence to solve other se-quence problems in bioinformatics.