Human limits in machine learning: prediction of potato yield and disease using soil microbiome data
Our study highlights the importance of incorporating diverse environmental features and careful data preprocessing in enhancing the predictive power of machine learning models for soil and biological phenotype connections. This approach can significantly contribute to advancing agricultural practices and soil health management.
Authors
- Rosa Aghdam (ORCID: https://orcid.org/0000-0001-9045-9592)
- Shan Shan (ORCID: https://orcid.org/0000-0003-4928-588X)
- Claudia Solís‐Lemus (ORCID: https://orcid.org/0000-0002-9789-8915)
- Xudong Tang (ORCID: https://orcid.org/0000-0002-7566-0666)
- Richard A. Lankau (ORCID: https://orcid.org/0000-0001-9995-328X)
Institutions
- University of Wisconsin–Madison (US)
- Wisconsin Institutes for Discovery (US)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-11-26
- DOI
- https://doi.org/10.1186/s12859-024-05977-2
- Citations
- 13
- Primary Topic
- Gene expression and cancer classification
- Type
- article
- Field-Weighted Citation Impact
- 3.04
Funders
- U.S. Department of Energy
- U.S. Department of Agriculture