Works (2)
Updated: January 18th, 2025 05:01
2024 article
Global genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates
Washburn, J. D., Varela, J. I., Xavier, A., Chen, Q., Ertl, D., Gage, J. L., … Leon, N. (2024, December 27). (M. Sillanpää, Ed.). GENETICS, Vol. 12.
Ed(s): M. Sillanpää
author keywords: genotype by environment; prediction; competition; maize; yield; phenotype
topics (OpenAlex): Genetics and Plant Breeding; Crop Yield and Soil Fertility; Genetic and phenotypic traits in livestock
open access via academic.oup.com (publisher PDF)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: November 22, 2024
2023 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
topics (OpenAlex): Gut microbiota and health; Gene expression and cancer classification; Genomics and Phylogenetic Studies
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)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: January 30, 2023