2022 review
Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies
[Review of ]. CURRENT OPINION IN PLANT BIOLOGY, 71.
author keywords: Machine learning; Deep learning; Plant-associated microbiome; Compositional data analysis
MeSH headings : Microbiota; Algorithms; Machine Learning; Plants
TL;DR:
The analysis is expanded to other fields to quantify the degree to which mitigation approaches improve the performance of ML and describe the mathematical basis for this.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
Added: January 30, 2023
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.