Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization

Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization

Background: Microbiome studies have uncovered associations between microbes and human, animal, and plant health outcomes. This has led to an interest in developing microbial interventions for treatment of disease and optimization of crop yields which requires identification of microbiome features th...

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Bibliographic Details
Main Authors: Chiniquy, D. (Author), Goren, E. (Author), He, Z. (Author), Liu, P. (Author), Prenni, J.E (Author), Schachtman, D.P (Author), Sheflin, A.M (Author), Tringe, S. (Author), Wang, C. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
Agricultural robots
Agriculture
animal
Animals
Bacteria
Categorical variables
Causal inference
Confounding Factors, Epidemiologic
Correlation between features
Diagnosis
Dynamic interaction
Estimation procedures
Feature extraction
Fertilizer applications
High-dimensional feature selection
human
Humans
Identification of individuals
Microbiome analysis
Microbiota
microflora
Next-generation sequencing
Nitrogen fertilizers
plant
Plant-microbe interactions
Plants
Population-level effects
Reference Standards
rhizosphere
Rhizosphere
Soils
standard
Standardization
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