Advancing Marine Genomics: The Role of Deep Learning in Deciphering Chelonia mydas Genetic Data

The urgent advancement of marine genomics is essential for the conservation of endangered species like Chelonia mydas (green sea turtle), and deep learning plays a pivotal role in deciphering their complex genetic data. Marine genomics as a field seems to be shifting more and more to the realm of big data especially with the introduction of new technologies in producing vast amounts of data. Such advancements have made it possible to manage huge datasets within genomics and this has provided artificial intelligence and particularly deep learning as a crucial method of acquiring insightful patterns. Review of the subject aims at focusing on the subject of deep learning methods and their usefulness in appearance and utilization in sub disciplinary areas of genomics of Chelonia mydas (green sea turtle). We introduce deep learning into marine genomics research by pointing out the existing gaps, as well as well-PSYCH detailed study fields. Moreover, we can only briefly mention the rather late incorporation of deep learning tools into marine genomics and the eminently discussed consequences for conservation and ecological science. By writing this review the authors envisage to let the biotechnology and genomic scientists to know the importance and applicability of using deep learning methods in Chelonia mydas genomics, the difficulty and prospects of this field. HIGHLIGHTS Emphasize the role of meta-omic integration and the large-scale DL model the power of conservation genetics, population monitoring and disease surveillance, as well as the assessment of habitat quality. Introduces Real Implementation of DL tools including DeepVariant, DeepSEA, DeepCpG and DeepSynergy that have enhanced the accuracy by the thirty percent and the computational speed of over forty percent. Introduces some of the most important frameworks based on DL which help researchers investigate the pattern of gene expression and epigenomic changes in Chelonia mydas such as DanQ, DeepChrome, and DeepHistone. Explains the possible application of DL in genomics and pharmacogenomics of Chelonia mydas, variant calling and epigenetics, gene expression analysis. Stresses DL approaches for identifying and predicting genetic variations, regulatory sites and drugs using more accuracy and capability. GRAPHICAL ABSTRACT