Gdaphen, R pipeline to identify the most important qualitative and quantitative predictor variables from phenotypic data
Abstract Background In individuals or animals suffering from genetic or acquired diseases, it is important to identify which clinical or phenotypic variables can be used to discriminate between disease and non-disease states, the response to treatments or sexual dimorphism. However, the data often s...
Main Authors: | Maria del Mar Muñiz Moreno, Claire Gavériaux-Ruff, Yann Herault |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2023-01-01
|
Series: | BMC Bioinformatics |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12859-022-05111-0 |
Similar Items
-
Development of a Low-Cost Distributed Computing Pipeline for High-Throughput Cotton Phenotyping
by: Vaishnavi Thesma, et al.
Published: (2024-02-01) -
Quantitative phenotyping and evaluation for lettuce leaves of multiple semantic components
by: Jianjun Du, et al.
Published: (2022-04-01) -
Field Phenotyping Monitoring Systems for High-Throughput: A Survey of Enabling Technologies, Equipment, and Research Challenges
by: Huali Yuan, et al.
Published: (2023-11-01) -
Emerging semantics to link phenotype and environment
by: Anne E. Thessen, et al.
Published: (2015-12-01) -
dbGaPCheckup: pre-submission checks of dbGaP-formatted subject phenotype files
by: Lacey W. Heinsberg, et al.
Published: (2023-03-01)