Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach.

ABSTRACT: Machine learning and modeling approaches have been used to classify protein sequences for a broad set of tasks including predicting protein function, structure, expression, and localization. Some recent studies have successfully predicted whether a given gene is expressed as mRNA or even translated to proteins potentially, but given that not all genes are expressed in every condition and tissue, the challenge remains to predict condition-specific expression. To address this gap, we developed a machine learning approach to predict tissue-specific gene expression across 23 different tissues in maize, solely based on DNA promoter and protein sequences. For class labels, we defined high and low expression levels for mRNA and protein abundance and optimized classifiers by systematically explori

SUBMITTER: Cho KT 

PROVIDER: S-EPMC9204276 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature