Deep Learning for the Classification of Genomic Signals
Joint Authors
Morales, J. Alejandro
Saldaña, Román
Santana-Castolo, Manuel H.
Torres-Cerna, Carlos E.
Borrayo, Ernesto
Mendizabal-Ruiz, Adriana P.
Vélez-Pérez, Hugo A.
Mendizabal-Ruiz, Gerardo
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-05
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Morales, J. Alejandro& Saldaña, Román& Santana-Castolo, Manuel H.& Torres-Cerna, Carlos E.& Borrayo, Ernesto& Mendizabal-Ruiz, Adriana P.…[et al.]. 2020. Deep Learning for the Classification of Genomic Signals. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1200688
Modern Language Association (MLA)
Morales, J. Alejandro…[et al.]. Deep Learning for the Classification of Genomic Signals. Mathematical Problems in Engineering No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1200688
American Medical Association (AMA)
Morales, J. Alejandro& Saldaña, Román& Santana-Castolo, Manuel H.& Torres-Cerna, Carlos E.& Borrayo, Ernesto& Mendizabal-Ruiz, Adriana P.…[et al.]. Deep Learning for the Classification of Genomic Signals. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1200688
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1200688