Machine learning model for predicting and interpreting soil enzyme functional composition-environment relationship
This item corresponds to our article: Fan, C., Song, Y., Mishra, U., Gautam, S., & Mayes, M. A. (2025). Harnessing the Power of Machine Learning and Omics to Identify Environmental Regulation on Microbial Functional Composition for Soil C, N, and P Cycling. Journal of Geophysical Research: Biogeosciences, 130(10). The included Jupyter Notebooks generate the tables and figures contained in the article.<br><br><br><hr><i>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</i>
Funding
NRT‐URoL: BRIDGES ‐ Building Resources for InterDisciplinary training in Genomic and Ecosystem Sciences
Directorate for Education & Human Resources
Find out more...DOE Sandia National Lab LDRD project
DOE EESM program’s Regional & Global Model Analysis (RGMA) DE-FOA-0003228
History
Related Materials
- 1.
- 2.
