Deep Learning Methodologies for Genomic Data Prediction: Review
- 10.2991/jaims.d.210512.001How to use a DOI?
- Deep learning; Genomics; DNA; Bioinformatics
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.
- © 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 -