On leveraging self-supervised learning for accurate HCV genotyping.

ABSTRACT: Hepatitis C virus (HCV) is a major global health concern, affecting millions of individuals worldwide. While existing literature predominantly focuses on disease classification using clinical data, there exists a critical research gap concerning HCV genotyping based on genomic sequences. Accurate HCV genotyping is essential for patient management and treatment decisions. While the neural models excel at capturing complex patterns, they still face challenges, such as data scarcity, that exist a lot in computational genomics. To overcome this challenges, this paper introduces an advanced deep learning approach for HCV genotyping based on the graphical representation of nucleotide sequences that outperforms classical approaches. Notably, it is effective for both partial and complete HCV genom

SUBMITTER: Fahmy AM 

PROVIDER: S-EPMC11224313 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature