Machine Learning for Mining Plant Functional Genes

Plants are constantly exposed to a wide array of biotic and abiotic stresses in their natural environments, posing significant challenges to agricultural productivity and global food security. Identifying and characterizing plant functional genes, along with elucidating their underlying molecular mechanisms in growth, development, and adaptive responses, are crucial steps in enhancing crop resilience, improving agronomic traits, boosting yields in crops, and fostering ecological conservation. The rapid advancement of genome sequencing technologies has led to an vast accumulation of data, providing rich resources for plant genomic research. Despite this, experimentally validated functional genes remain scarce, and a significant proportion of plant genes are still uncharacterized, presenting a critical gap in our understanding of plant biology. Bridging this gap is vital for transforming genomic data into actionable insights for sustainable agriculture and crop improvement.

In recent years, machine learning has gained widespread adoption and demonstrated great potential in advancing biological research, particularly in the realm of plant genomics. When integrated with extensive biological datasets—such as multi-omics, proteomics, and gene expression profiles—these methods prove highly effective for unraveling the genetic structures and intricate molecular mechanisms that govern key plant traits. This capability not only enriches our understanding of plant biology but also propels the discovery of novel genes, regulatory pathways, and adaptive mechanisms, thereby facilitating the development of resilient, high-yield crops.

This research topic aims to provide a comprehensive overview of the latest advancements in applying machine learning to functional gene discovery in plants. We invite contributions that highlight how integrating diverse biological data with machine learning models can accelerate the identification of genes associated with critical traits, reveal novel regulatory mechanisms, and enhance genetic selection for traits such as yield, resilience, and stress resistance.

This Research Topic includes, but is not limited to, the following:
• Reviews of machine learning algorithms for plant functional gene mining.
• Machine learning algorithms for constructing gene regulatory networks and identifying plant functional genes.
• Developing machine learning models for analyzing multi-omics data.
• Development of multi-omics data analysis tools and platforms based on machine learning.
• Development of plant functional gene databases incorporating gene regulatory network data and machine learning-based functional gene predictions.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: machine learning, plant biology, functional genes, crop improvement, multi-omics

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.