Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies

Sebastiano Busato, Max Gordon, Meenal Chaudhari, Ib Thorsgaard Jensen, Turgut Yigit Akyol, Stig Uggerhøj Andersen, Cranos Williams

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisReviewForskningpeer review

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.
OriginalsprogEngelsk
Artikelnummer102326
TidsskriftCurrent Opinion in Plant Biology
Vol/bind71
Antal sider11
ISSN1369-5266
DOI
StatusUdgivet - feb. 2023

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