Machine and Deep Learning Approaches in Genome: Review Article | ||
| Alfarama Journal of Basic & Applied Sciences | ||
| Article 11, Volume 2, Issue 1, January 2021, Pages 105-113 PDF (509.91 K) | ||
| Document Type: Review Article | ||
| DOI: 10.21608/ajbas.2020.34160.1023 | ||
| Authors | ||
| Bossy M. Mostafa* 1; Noha El-Attar2; Samy Abd-Elhafeez3; Wael Awad3 | ||
| 1Department of Mathematics and Computer Science, Faculty of Science, PortSaid University, Port Said, Egypt | ||
| 2Department of Information Systems, Faculty of Computers and AI, Benha University, Benha, Egypt | ||
| 3Department of Mathematics and Computer Science, Faculty of Science, Portsaid University, Portsaid, Egypt. | ||
| Abstract | ||
| Throughout the years Machine Learning (ML) has increased a lot of consideration on ordinary products as search, filters, recognition and recently genomics. Various strategies incorporate sophisticated artificial neural system designs and are all known as applications of Deep Learning (DL). These days, deep learning could be a current and a fortifying field of machine learning. Deep Learning models have fair been shown prepared for both enhancing data encoding simplicity and prescient design execution over elective methodologies. Also deep learning techniques have been shown to reflect and learn unsurprising relationships in various different types of data and to guarantee that the future of genomics research and precise medicine applications will change. DL applications in genomic field is rapidly developed. This review presents a clarification for machine learning and deep learning methods utilized in genomics. And the main goal is to show a detailed comprehensive overview on the available ML and DL techniques used in genomics. | ||
| Keywords | ||
| Genomics; DNA; Neural Network; Deep Learning | ||
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