Key points are not available for this paper at this time.
Plant bioactives hold immense potential in the medicine and food industry. The recent advancements in omics applied in deciphering specialized metabolic pathways underscore the importance of high-quality genome releases and the wealth of data in metabolomics and transcriptomics. While harnessing data, whether integrated or standalone, has proven successful in unveiling plant natural product (PNP) biosynthetic pathways, the democratization of machine learning in biology opens exciting new opportunities for enhancing the exploration of these pathways. This review highlights the recent breakthroughs in disrupting plant-specialized biosynthetic pathways through the utilization of omics data harnessing and machine learning techniques.
Durand et al. (Thu,) studied this question.