About this Research Topic
The field of plant-associated microbiomes has garnered significant attention due to its potential to address global food insecurity, particularly in low and middle-income countries. As the global population continues to grow, improving crop yield and productivity through advanced breeding programs has become imperative. Plants and their associated microbial communities have co-evolved over millennia, forming intricate relationships that significantly impact plant health and fitness. Recent studies have highlighted the benefits of these microbial communities, such as enhanced growth, improved nutrient uptake, and increased tolerance to environmental stresses. Despite these advancements, traditional methods of analyzing multi-omics data—such as meta-genomics, meta-transcriptomics, and meta-proteomics—are often inadequate. These methods struggle with false positives and fail to capture the interaction effects between variables, leaving gaps in our understanding of how microbiomes influence plant phenotypes. The advent of machine-learning (ML) algorithms has revolutionized this field, offering new ways to analyze complex microbiome data and predict their impact on plant traits.
This research topic aims to gather experts from plant microbiome, plant breeding, and computational biology fields to discuss the latest advancements in understanding plant-microbiome interactions and their role in enhancing crop yield and productivity. The primary objective is to explore how sophisticated ML approaches can be leveraged to gain deeper insights into these interactions. Specific questions include how ML algorithms can predict microbial dynamics and functions, recognize microbial assemblies under various environmental conditions, and engineer microbiomes to produce desired phenotypic traits. Additionally, the research will test hypotheses related to the effectiveness of ML models in handling the compositionality, sparsity, and high dimensionality of microbiome data to generate accurate predictions for targeted traits.
To gather further insights into the boundaries of this research, we welcome articles addressing, but not limited to, the following themes:
- The use of ML algorithms to predict the dynamics and functions of plant-associated microbiomes.
- The use of ML algorithms to recognize the assemblies of plant-associated microbiomes under diverse environmental conditions.
- Potential use of ML algorithms in microbiome engineering.
- Advanced models of ML for plant phenotyping.
- The contribution of computer vision and ML methods to correlating plant microbiome and its phenotypic traits.
- Big data and predictive analytics in plant-microbiome interactions and characteristics.
- Contribution of ML methods to the development of sustainable agricultural practices.
We look forward to receiving your contributions to this special issue.
This research topic aims to gather experts from plant microbiome, plant breeding, and computational biology fields to discuss the latest advancements in understanding plant-microbiome interactions and their role in enhancing crop yield and productivity. The primary objective is to explore how sophisticated ML approaches can be leveraged to gain deeper insights into these interactions. Specific questions include how ML algorithms can predict microbial dynamics and functions, recognize microbial assemblies under various environmental conditions, and engineer microbiomes to produce desired phenotypic traits. Additionally, the research will test hypotheses related to the effectiveness of ML models in handling the compositionality, sparsity, and high dimensionality of microbiome data to generate accurate predictions for targeted traits.
To gather further insights into the boundaries of this research, we welcome articles addressing, but not limited to, the following themes:
- The use of ML algorithms to predict the dynamics and functions of plant-associated microbiomes.
- The use of ML algorithms to recognize the assemblies of plant-associated microbiomes under diverse environmental conditions.
- Potential use of ML algorithms in microbiome engineering.
- Advanced models of ML for plant phenotyping.
- The contribution of computer vision and ML methods to correlating plant microbiome and its phenotypic traits.
- Big data and predictive analytics in plant-microbiome interactions and characteristics.
- Contribution of ML methods to the development of sustainable agricultural practices.
We look forward to receiving your contributions to this special issue.
Keywords: plant phenotyping, plant breeding, machine learning models, metagenomics, high throughput sequencing, crop production, sustainable agricultural practices, plant-associated microbiomes
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.
