Investigating alignment-free machine learning methods for HIV-1 subtype classification.

ABSTRACT:

Motivation

Many viruses are organized into taxonomies of subtypes based on their genetic similarities. For human immunodeficiency virus 1 (HIV-1), subtype classification plays a crucial role in infection management. Sequence alignment-based methods for subtype classification are impractical for large datasets because they are costly and time-consuming. Alignment-free methods involve creating numerical representations for genetic sequences and applying statistical or machine learning methods. Despite their high overall accuracy, existing models perform poorly on less common subtypes. Furthermore, there is limited work investigating the impact of sequence vectorization methods, in particular natural language-inspired embedding methods, on HIV-1 subtype classification.

Results

SUBMITTER: Wade KE 

PROVIDER: S-EPMC11371153 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature