Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data.

ABSTRACT: The advances of single-cell transcriptomic technologies have led to increasing use of single-cell RNA sequencing (scRNA-seq) data in large-scale patient cohort studies. The resulting high-dimensional data can be summarized and incorporated into patient outcome prediction models in several ways; however, there is a pressing need to understand the impact of analytical decisions on such model quality. In this study, we evaluate the impact of analytical choices on model choices, ensemble learning strategies and integrate approaches on patient outcome prediction using five scRNA-seq COVID-19 datasets. First, we examine the difference in performance between using single-view feature space versus multi-view feature space. Next, we survey multiple learning platforms from classical machine learning

SUBMITTER: Cao Y 

PROVIDER: S-EPMC10199760 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature