Classification of group A rotavirus VP7 and VP4 genotypes using random forest.

ABSTRACT: Introduction: Group A rotaviruses are major pathogens in causing severe diarrhea in young children and neonates of many different species of animals worldwide and group A rotavirus sequence data are becoming increasingly available over time. Different methods exist that allow for rotavirus genotyping, but machine learning methods have yet to be explored. Usage of machine learning algorithms such as random forest alongside alignment-based methodology may allow for both efficient and accurate classification of circulating rotavirus genotypes through the dual classification system. Methods: Random forest models were trained on positional features obtained from pairwise and multiple sequence alignment and cross-validated using methods of repeated 10-fold cross-validation thrice a

SUBMITTER: Tran H 

PROVIDER: S-EPMC10267748 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature