Dimensionality Reduction of Binding Site Sequence Data on Human Genome Using a Deep Autoencoder Neural Network

Bankikoshki, Hossein; Seyyedsalehi, Seyed Ali; Zare Mirakabad, Fatemeh

doi:10.22041/ijbme.2018.75885.1294

Document Type : Full Research Paper

Authors

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

https://doi.org/10.22041/ijbme.2018.75885.1294

Abstract

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.

Keywords

Main Subjects

Bioinformatics / Biomedical Informatics / Medical Informatics / Health Informatics

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Iranian Journal of Biomedical Engineering

Dimensionality Reduction of Binding Site Sequence Data on Human Genome Using a Deep Autoencoder Neural Network

References

References

Volume 11, Issue 3
September 2017
Pages 219-230

Dimensionality Reduction of Binding Site Sequence Data on Human Genome Using a Deep Autoencoder Neural Network

References

References

Volume 11, Issue 3September 2017Pages 219-230

Volume 11, Issue 3
September 2017
Pages 219-230