Advances in sequencing technologies enable scientists to obtain molecular features of genes in high-dimensionality. Features of individual gene like expression, methylation, histone modification, evolutionary signals and sequence itself provides higher resolution for distinguishing annotated genes. Along plant genomes, the percentage of annotated genes with experimental evidence is in a low rate. Authors classified Arabidopsis annotated genes into Specialized Metabolism (SM) or General Metabolism (GM), where refers to gene in response to specific stimulus or playing as a central role in metabolism pathway. With the integration of 10,243 features, authors conducted machine learning algorithms, Random Forest and SVM, to make predictions on identifying genes involved in GM or SM. The study provides a framework on gene function prediction using accessible features from molecular level. PNAS: https://www.pnas.org/content/1...