| Autor: |
Kokiladevi Alagarswamy, Wenjie Shi, Aishwarya Boini, Nouredin Messaoudi, Vincent Grasso, Thomas Cattabiani, Bruce Turner, Roland Croner, Ulf D. Kahlert, Andrew Gumbs |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
BioMedInformatics, Vol 4, Iss 3, Pp 1757-1772 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2673-7426 |
| DOI: |
10.3390/biomedinformatics4030096 |
| Popis: |
In this scoping review, we delve into the transformative potential of artificial intelligence (AI) in addressing challenges inherent in whole-genome sequencing (WGS) analysis, with a specific focus on its implications in oncology. Unveiling the limitations of existing sequencing technologies, the review illuminates how AI-powered methods emerge as innovative solutions to surmount these obstacles. The evolution of DNA sequencing technologies, progressing from Sanger sequencing to next-generation sequencing, sets the backdrop for AI’s emergence as a potent ally in processing and analyzing the voluminous genomic data generated. Particularly, deep learning methods play a pivotal role in extracting knowledge and discerning patterns from the vast landscape of genomic information. In the context of oncology, AI-powered methods exhibit considerable potential across diverse facets of WGS analysis, including variant calling, structural variation identification, and pharmacogenomic analysis. This review underscores the significance of multimodal approaches in diagnoses and therapies, highlighting the importance of ongoing research and development in AI-powered WGS techniques. Integrating AI into the analytical framework empowers scientists and clinicians to unravel the intricate interplay of genomics within the realm of multi-omics research, paving the way for more successful personalized and targeted treatments. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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