Phenotype prediction in plants is improved by integrating large-scale transcriptomic datasets.

ABSTRACT: Research on the dynamic expression of genes in plants is important for understanding different biological processes. We used the large amounts of transcriptomic data from various plant sample sources that are publicly available to investigate whether the expression levels of a subset of highly variable genes (HVGs) can be used to accurately identify the phenotypes of plants. Using maize (Zea mays L.) as an example, we built machine learning (ML) models to predict phenotypes using a gene expression dataset of 21 612 bulk RNA sequencing samples. We showed that the ML models achieved excellent prediction accuracy using only the HVGs to identify different phenotypes, including tissue types, developmental stages, cultivars and stress conditions. By ML models, several important functional

SUBMITTER: Wu Z 

PROVIDER: S-EPMC11672113 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature