ReviewCurrent opinion in plant biology2023
Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies.
Review in Current opinion in plant biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence at the gut-oral microbiota frontier: mapping machine learning tools for gastric cancer risk prediction.Biomedical engineering online · 2025Pooled it
- Beyond just correlation: causal machine learning for the microbiome, from prediction to health policy with econometric tools.Frontiers in microbiology · 2025Pooled it
- Challenges and Future Directives of Synthetic Biology in Engineering Plant-Microbe Partnerships for Sustainable Agriculture.Biotechnology and bioengineering · 2026Review
- Environmental stress strengthens plant-mycorrhizal associations: A novel extension of the stress gradient hypothesis.Ecology · 2026Article
- From association to causation: a decision-aware framework for reproducible biomarker discovery and precision intervention design in the human gut microbiome.Briefings in bioinformatics · 2026Article
- Review
- Latent Dirichlet Allocation reveals tomato root-associated bacterial interactions responding to hairy root disease.Environmental microbiome · 2025Article
- Genome-resolved metagenomics from short-read sequencing data in the era of artificial intelligence.Functional & integrative genomics · 2025Review
- Order among chaos: High throughput MYCroplanters can distinguish interacting drivers of host infection in a highly stochastic system.PLoS pathogens · 2025Article
- Machine learning models reveal Saccharomyces yeasts are associated with poor piglet growth in early development.Journal of animal science · 2025Article
- The human microbiome in clinical translation: from bench to bedside.Frontiers in microbiology · 2025Review
- Graph Neural Network-Based Approaches for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Nine (not so simple) steps: a practical guide to using machine learning in microbial ecology.mBio · 2024Review
- Unveiling the Connection between Microbiota and Depressive Disorder through Machine Learning.International journal of molecular sciences · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The plant-associated microbiome is a key component of plant systems, contributing to their health, growth, and productivity. The application of machine learning (ML) in this field promises to help untangle the relationships involved. However, measurements of microbial communities by high-throughput sequencing pose challenges for ML. Noise from low sample sizes, soil heterogeneity, and technical factors can impact the performance of ML. Additionally, the compositional and sparse nature of these datasets can impact the predictive accuracy of ML. We review recent literature from plant studies to illustrate that these properties often go unmentioned. We expand our analysis to other fields to quantify the degree to which mitigation approaches improve the performance of ML and describe the mathematical basis for this. With the advent of accessible analytical packages for microbiome data including learning models, researchers must be familiar with the nature of their datasets.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.