Version 1
: Received: 18 March 2024 / Approved: 18 March 2024 / Online: 19 March 2024 (02:25:51 CET)
How to cite:
Alagarswamy, K.; Boini, A.; Messaoudi, N.; Grasso, V.; Turner, B.; croner, R.; Gumbs, A. Can AI-Powered Whole Genome Sequencing Be Used Routinely for Personalized Decision Support in Surgical Oncology? – A Scoping Review. Preprints2024, 2024031050. https://doi.org/10.20944/preprints202403.1050.v1
Alagarswamy, K.; Boini, A.; Messaoudi, N.; Grasso, V.; Turner, B.; croner, R.; Gumbs, A. Can AI-Powered Whole Genome Sequencing Be Used Routinely for Personalized Decision Support in Surgical Oncology? – A Scoping Review. Preprints 2024, 2024031050. https://doi.org/10.20944/preprints202403.1050.v1
Alagarswamy, K.; Boini, A.; Messaoudi, N.; Grasso, V.; Turner, B.; croner, R.; Gumbs, A. Can AI-Powered Whole Genome Sequencing Be Used Routinely for Personalized Decision Support in Surgical Oncology? – A Scoping Review. Preprints2024, 2024031050. https://doi.org/10.20944/preprints202403.1050.v1
APA Style
Alagarswamy, K., Boini, A., Messaoudi, N., Grasso, V., Turner, B., croner, R., & Gumbs, A. (2024). <strong></strong>Can AI-Powered Whole Genome Sequencing Be Used Routinely for Personalized Decision Support in Surgical Oncology? – A Scoping Review. Preprints. https://doi.org/10.20944/preprints202403.1050.v1
Chicago/Turabian Style
Alagarswamy, K., Roland croner and Andrew Gumbs. 2024 "<strong></strong>Can AI-Powered Whole Genome Sequencing Be Used Routinely for Personalized Decision Support in Surgical Oncology? – A Scoping Review" Preprints. https://doi.org/10.20944/preprints202403.1050.v1
Abstract
INTRODUCTION : 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 surgical oncology. METHODS: Scoping review of whole genomic sequencing and artificial intelligence.DISCUSSION : 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 by these technologies. 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. CONCLUSIONS : This review underscores the significance of multimodal approaches in diagnoses and therapies, highlighting the imperative for 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 personalized and targeted treatments in surgical oncology and perhaps beyond.
Keywords
whole genomic sequencing; proteomics; transcriptomics; machine learning; deep learning; modalities
Subject
Biology and Life Sciences, Biology and Biotechnology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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