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Deep Learning for the Classification of Genomic Signals

Author

Listed:
  • J. Alejandro Morales
  • Román Saldaña
  • Manuel H. Santana-Castolo
  • Carlos E. Torres-Cerna
  • Ernesto Borrayo
  • Adriana P. Mendizabal-Ruiz
  • Hugo A. Vélez-Pérez
  • Gerardo Mendizabal-Ruiz

Abstract

Genomic signal processing (GSP) is based on the use of digital signal processing methods for the analysis of genomic data. Convolutional neural networks (CNN) are the state-of-the-art machine learning classifiers that have been widely applied to solve complex problems successfully. In this paper, we present a deep learning architecture and a method for the classification of three different functional genome types: coding regions (CDS), long noncoding regions (LNC), and pseudogenes (PSD) in genomic data, based on the use of GSP methods to convert the nucleotide sequence into a graphical representation of the information contained in it. The obtained accuracy scores of 83% and 84% when classifying between CDS vs. LNC and CDS vs. PSD, respectively, indicate the feasibility of employing this methodology for the classification of these types of sequences. The model was not able to differentiate from PSD and LNC. Our results indicate the feasibility of employing CNN with GSP for the classification of these types of DNA data.

Suggested Citation

  • J. Alejandro Morales & Román Saldaña & Manuel H. Santana-Castolo & Carlos E. Torres-Cerna & Ernesto Borrayo & Adriana P. Mendizabal-Ruiz & Hugo A. Vélez-Pérez & Gerardo Mendizabal-Ruiz, 2020. "Deep Learning for the Classification of Genomic Signals," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-9, May.
    • Handle: RePEc:hin:jnlmpe:7698590
    DOI: 10.1155/2020/7698590
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