Deciphering plant transcriptomes: Leveraging machine learning for deeper insights

Abstract

Plant transcriptomics is an important field for understanding the dynamics of gene expression, regulatory mechanisms and interactions underlying plant development and stress responses. Despite advances in high-throughput sequencing technologies, the vast amount of transcriptomic data poses significant challenges to traditional methods of analysis and limits the generation of meaningful biological insights. This review addresses the integration of machine learning (ML) techniques in plant transcriptomics and emphasizes their potential to transform data analysis and interpretation. We analyzed different ML methods and their applications in the identification of differentially expressed genes (DEGs), the elucidation of functional annotations and the reconstruction of regulatory networks. The main results show that ML approaches improve the accuracy of transcriptome analyses and facilitate the identification of novel gene functions and regulatory interactions that may be overlooked by conventional methods. The implications of this work are profound. The use of ML can lead to a deeper understanding of plant biology and significantly impact crop improvement strategies. By revealing the complexity of stress tolerance and developmental processes, ML applications can inform breeding programs and improve agricultural resilience. Future research should focus on refining ML algorithms, improving the accessibility of these tools for plant scientists, and fostering interdisciplinary collaborations to maximize the potential of ML in plant transcriptomics.