ArticleFrontiers in microbiology2017
Metagenome-Wide Association Study and Machine Learning Prediction of Bulk Soil Microbiome and Crop Productivity.
Article in Frontiers in microbiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 1 of them a synthesis 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
69 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Bibliometric analysis of the application of artificial intelligence techniques in bacteriology: a PRISMA-guided research agenda.Frontiers in microbiology · 2025Pooled it
- Machine learning reveals biocontrol agents shaping disease outcome in natural Arabidopsis populations.Nature communications · 2026Article
- Soil microbiome prediction using traditional machine learning and deep learning models.Scientific reports · 2026Article
- Plant-soil-microbiome interactions: mechanisms, advances, and challenges in sustainable agriculture and healthy agroecosystems.Frontiers in microbiology · 2026Review
- Metagenomic Tools as Predictive Approaches to Decipher Soil and Rhizosphere Microbial Communities and Their Functions for Sustainable Agriculture.International journal of genomics · 2026Review
- A genomic view of Earth's biomes.Nature reviews. Genetics · 2026Review
- Unveiling the role of soil microorganisms in indicating paddy soil health via metagenomics combined with machine learning.ISME communications · 2026Article
- Uncertainty Modeling Outperforms Machine Learning for Microbiome Data Analysis.bioRxiv : the preprint server for biology · 2025Article
- Phylogeny-metabolism dual-directed single-cell genomics for dissecting and mining ecosystem function by FISH-scRACS-seq.Innovation (Cambridge (Mass.)) · 2025Article
- Engineering plant holobionts for climate-resilient agriculture.The ISME journal · 2025Review
- Harnessing the plant microbiome for sustainable crop production.Nature reviews. Microbiology · 2025Review
- Growth promotion and stress tolerance of soybean plants driven by seed treatment with synthetic bacterial community of soybean-associated beneficial bacteria.Frontiers in plant science · 2025Article
- Exploring and exploiting the rice phytobiome to tackle climate change challenges.Plant communications · 2024Review
- Transforming Agricultural and Sulfur Waste into Fertilizer: Assessing the Short-Term Effects on Microbial Biodiversity via a Metagenomic Approach.Life (Basel, Switzerland) · 2024Article
- DGCNN approach links metagenome-derived taxon and functional information providing insight into global soil organic carbon.NPJ biofilms and microbiomes · 2024Article
- Development of a logic regression-based approach for the discovery of host- and niche-informative biomarkers inApplied and environmental microbiology · 2024Article
- A practical introduction to holo-omics.Cell reports methods · 2024Review
- Interpretable machine learning reveals microbiome signatures strongly associated with dairy cow milk urea nitrogen.iScience · 2024Article
- Interpretable machine learning decodes soil microbiome's response to drought stress.Environmental microbiome · 2024Article
- Exploring Cereal Metagenomics: Unravelling Microbial Communities for Improved Food Security.Microorganisms · 2024Review
9 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Areas within an agricultural field in the same season often differ in crop productivity despite having the same cropping history, crop genotype, and management practices. One hypothesis is that abiotic or biotic factors in the soils differ between areas resulting in these productivity differences. In this study, bulk soil samples collected from a high and a low productivity area from within six agronomic fields in Illinois were quantified for abiotic and biotic characteristics. Extracted DNA from these bulk soil samples were shotgun sequenced. While logistic regression analyses resulted in no significant association between crop productivity and the 26 soil characteristics, principal coordinate analysis and constrained correspondence analysis showed crop productivity explained a major proportion of the taxa variance in the bulk soil microbiome. Metagenome-wide association studies (MWAS) identified more
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.