Machine learning-based gene expression biomarkers to distinguish Zika and Dengue virus infections: implications for diagnosis.

ABSTRACT: Zika virus (ZIKV) and Dengue virus (DENV) infections cause severe disease in humans and are significant socio-economic burden worldwide. These flavivirus infections are difficult to diagnose serologically due to antigenic overlap. The phylogenetic analysis shows that ZIKV clusters with DENVs at a higher node of the phylogenetic tree with significant genomic and structural similarity. Our study aims to identify gene biomarkers for the classification of Dengue and Zika viral infections using machine learning algorithms and bioinformatics analysis. The gene expression count matrix for single-cell RNA sequencing dataset GSE110496 was analyzed using binary classifiers, namely Logistic regression, Support Vector Machines, Random Forest, and Decision trees. The GSE110496 dataset represents a uniq

SUBMITTER: Zeba A 

PROVIDER: S-EPMC11502647 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature