Machine Learning Prediction of Nitrification From Ammonia- and Nitrite-Oxidizer Community Structure.

ABSTRACT: Accurately modeling nitrification and understanding the role specific ammonia- or nitrite-oxidizing taxa play in it are of great interest and importance to microbial ecologists. In this study, we applied machine learning to 16S rRNA sequence and nitrification potential data from an experiment examining interactions between cropping systems and rhizosphere on microbial community assembly and nitrogen cycling processes. Given the high dimensionality of microbiome datasets, we only included nitrifers since only a few taxa are capable of ammonia and nitrite oxidation. We compared the performance of linear and nonlinear algorithms with and without qPCR measures of bacterial and archaea ammonia monooxygenase subunit A (amoA) gene abundance. Our feature selection process facilitated the id

SUBMITTER: Lee C 

PROVIDER: S-EPMC9309558 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature