ArticleScientific reports2019
Statistical model choice including variable selection based on variable importance: A relevant way for biomarkers selection to predict meat tenderness.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 21 citations in OpenAlex.
- UHPLC-MS/MS-Based Metabolomics Identifies Freshness Biomarkers and Temporal Spoilage Threshold in Refrigerated Goose Meat.Foods (Basel, Switzerland) · 2025Article
- Exploring the variable importance in random forests under correlations: a general concept applied to donor organ quality in post-transplant survival.BMC medical research methodology · 2023Article
- Current State of Metabolomics Research in Meat Quality Analysis and Authentication.Foods (Basel, Switzerland) · 2021Review
- A machine learning workflow for raw food spectroscopic classification in a future industry.Scientific reports · 2020Article
- Various Statistical Approaches to Assess and Predict Carcass and Meat Quality Traits.Foods (Basel, Switzerland) · 2020Article
- New Approach Studying Interactions Regarding Trade-Off between Beef Performances and Meat Qualities.Foods (Basel, Switzerland) · 2019Article
- Binary Logistic Regression Modeling of Voice Impairment and Voice Assessment in Iranian Patients with Nonlaryngeal Head-and-Neck Cancers after Chemoradiation Therapy: Objective and Subjective Voice Evaluation.Journal of medical signals and sensorsArticle
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we describe a new computational methodology to select the best regression model to predict a numerical variable of interest Y and to select simultaneously the most interesting numerical explanatory variables strongly linked to Y. Three regression models (parametric, semi-parametric and non-parametric) are considered and estimated by multiple linear regression, sliced inverse regression and random forests. Both the variables selection and the model choice are computational. A measure of importance based on random perturbations is calculated for each covariate. The variables above a threshold are selected. Then a learning/test samples approach is used to estimate the Mean Square Error and to determine which model (including variable selection) is the most accurate. The R package modvarsel (MODel and VARiable SELection) implements this computational approach and applies to any regression datasets. After checking the good behavior of the methodology on simulated data, the R package is used to select the proteins predictive of meat tenderness among a pool of 21 candidate proteins assayed in semitendinosus muscle from 71 young bulls. The biomarkers were selected by linear regression (the best regression model) to predict meat tenderness. These biomarkers, we confirm the predominant role of heat shock proteins and metabolic ones.
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