Document Type : Full Research Paper


1 MSc Student, Bioelectric Department, Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran

2 Associate Professor, Bioelectric Department, Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran

3 Assistant Professor, Faculty of Mathematics & Computer Sciences, Amirkabir University of Technology, Tehran, Iran


The use of genomic nucleotide sequences as biochemical signals in machine learning methods is possible by converting these sequences into numerical codes. This conversion results in an unrealistic increase in the dimension of the data and encounters some data analysis operations such as visualization and feature extraction with constraints. Therefore, one should use the dimensionality reduction technics in order to return the data to its real dimension. In this study, a deep autoencoder neural network has been used to reduce the dimension of binding site sequence data on the human genome. In order to determine whether the information of real data is preserved in compressed data, we perform a two-class classification using a support vector machine. The results show that information is almost entirely preserved in compression. Then, compressed data is used for visualization as well as feature selection by analysis of variance. The results show that the first, the tenth and eighth positions in the sequences are the most informative positions. While the majority of the previous works deal with gene expression data of microarrays and compare a few dimension reduction algorithms, this paper for the first time uses an autoencoder on nucleotide sequence data and provides a comprehensive comparison between the performance of the dimension reduction technics and machine learning algorithms.


Main Subjects

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