Deep Learning Methodologies for Genomic Data Prediction: Review

Authors

Yusuf Aleshinloye Abass^*, , Steve A. Adeshina

Department of Computer Science, Nile University of Nigeria, Nigeria

^*Corresponding author. Email: yusuf.abass@nileuniversity.edu.ng

DOI

10.2991/jaims.d.210512.001How to use a DOI?

Keywords

Deep learning; Genomics; DNA; Bioinformatics

Abstract

The last few years have seen an advancement in genomic research in bioinformatics. With the introduction of high-throughput sequencing techniques, researchers now can analyze and produce a large amount of genomic datasets and this has aided the classification of genomic studies as a “big data” discipline. There is a need to develop a robust and powerful algorithm and deep learning methodologies can provide better performance accuracy than other computational methodologies. In this review, we captured the most frequently used deep learning architectures for the genomic domain. We outline the limitations of deep learning methodologies when dealing with genomic data and we conclude that advancement in deep learning methodologies will help rejuvenate genomic research and build a better architecture that will promote a genomic task.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Yusuf Aleshinloye Abass
AU  - Steve A. Adeshina
PY  - 2021
DA  - 2021/05/19
TI  - Deep Learning Methodologies for Genomic Data Prediction: Review
JO  - Journal of Artificial Intelligence for Medical Sciences
SP  - 1
EP  - 11
VL  - 2
IS  - 1-2
SN  - 2666-1470
UR  - https://doi.org/10.2991/jaims.d.210512.001
DO  - 10.2991/jaims.d.210512.001
ID  - Abass2021
ER  -