ArticleScientific reports2024
A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 112 citations in OpenAlex.
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- Noninvasive PPROM risk stratification with explainable AI using routine antenatal CRP and albumin.NPJ digital medicine · 2026Article
- A SHAP-based machine learning approach to decoding the relationship between running posture features and tibial load in novice runners with MTSS: towards accurate and intelligent rehabilitation strategies.BMC musculoskeletal disorders · 2026Article
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- Integrative machine learning approach for identifying genes associated with quantitative traits: A soybean (Glycine max) yield case study.The plant genome · 2026Article
- Machine Learning-Enhanced Evaluation of Handheld Laser-Induced Breakdown Spectroscopy (LIBS) Analytical Performance for Multi-Element Analysis of Rock Samples.Sensors (Basel, Switzerland) · 2026Article
- Combined Experimental and Machine Learning Study on the Interplay between Delignification and Mechanical Properties for Improved Poplar Wood Reconstruction.ACS applied materials & interfaces · 2026Article
- Bayesian neural networks for genomic prediction: uncertainty quantification and SNP interpretation with SHAP and GWAS.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Twelve tips for using educational data mining and machine learning to predict performance in high-stakes health professions education exams.Journal of education and health promotion · 2026Review
- Development and validation of machine learning models based on blood routine tests and tumor markers in early screening of primary bronchogenic lung cancer.Translational lung cancer research · 2025Article
- Landslide susceptibility assessment using hybrid geospatial, frequency ratio, and AHP models in Souk Ahras province, Northeastern of Algeria.Scientific reports · 2025Article
- Predicting water quality index using stacked ensemble regression and SHAP based explainable artificial intelligence.Scientific reports · 2025Article
- AdaptiveGS: an explainable genomic selection framework based on adaptive stacking ensemble machine learning.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- A Bio-Inspired Adaptive Probability IVYPSO Algorithm with Adaptive Strategy for Backpropagation Neural Network Optimization in Predicting High-Performance Concrete Strength.Biomimetics (Basel, Switzerland) · 2025Article
- Machine learning analysis of greenhouse gas sources impacting Africa's food security nexus.Scientific reports · 2025Article
- A measurement-based framework integrating machine learning and morphological dynamics for outdoor thermal regulation.International journal of biometeorology · 2025Article
- Research on early warning model of coal spontaneous combustion based on interpretability.Scientific reports · 2025Article
- Integration of RNAseq transcriptomics andChemical science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To explore a robust tool for advancing digital breeding practices through an artificial intelligence-driven phenotype prediction expert system, we undertook a thorough analysis of 11 non-linear regression models. Our investigation specifically emphasized the significance of Support Vector Regression (SVR) and SHapley Additive exPlanations (SHAP) in predicting soybean branching. By using branching data (phenotype) of 1918 soybean accessions and 42 k SNP (Single Nucleotide Polymorphism) polymorphic data (genotype), this study systematically compared 11 non-linear regression AI models, including four deep learning models (DBN (deep belief network) regression, ANN (artificial neural network) regression, Autoencoders regression, and MLP (multilayer perceptron) regression) and seven machine learning models (e.g., SVR (support vector regression), XGBoost (eXtreme Gradient Boosting) regression, Random Forest regression, LightGBM regression, GPs (Gaussian processes) regression, Decision Tree regression, and Polynomial regression). After being evaluated by four valuation metrics: R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.