Deep Learning Methodologies for Genomic Data Prediction: Review

Authors

  • Yusuf Aleshinloye Abass Department of Computer Science, Nile University of Nigeria, Nigeria https://orcid.org/0000-0001-8388-7361
  • Steve A. Adeshina Department of Computer Science, Nile University of Nigeria, Nigeria

DOI:

https://doi.org/10.2991/jaims.d.210512.001

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.

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Published

2021-05-05

How to Cite

1.
Abass YA, Adeshina SA. Deep Learning Methodologies for Genomic Data Prediction: Review. JAIMS [Internet]. 2021 May 5 [cited 2023 Feb. 4];2(1-2):1-11. Available from: http://oapublishing-jaims.com/jaims/article/view/61