Research graph
References from Genomic prediction through machine learning and neural networks for traits with epistasis. Local targets link to admitted publications; unresolved targets remain external evidence.
10.1093/genetics/157.4.1819
10.1093/genetics/157.4.1819 · External reference
Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data
10.1016/j.jplph.2020.153354 · 2021 · External reference
10.1007/978-81-322-2316-0
10.1007/978-81-322-2316-0 · External reference
Breeding Jatropha curcas by genomic selection: A pilot assessment of the accuracy of predictive models
2017 · External reference
Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods
2018 · External reference
Potential of genomic selection in mass selection breeding of an allogamous crop: An empirical study to increase yield of common buckwheat
10.3389/fpls.2018.00276 · 2018 · External reference
Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding
10.3389/fpls.2018.01934 · 2019 · External reference
Selective efficiency of genome-wide selection in Coffea canephora breeding
10.1007/s11295-020-01433-3 · 2020 · External reference
10.35418/2526-4117/v1n2a1
10.35418/2526-4117/v1n2a1 · External reference
Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy: a machine learning approach
2020 · External reference
Unresolved reference
2018 · External reference
Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms
10.1590/1678-992x-2020-0021 · 2021 · External reference
Tree and spline based association analysis of gene-gene interaction models for ischemic stroke
10.1002/sim.1749 · 2004 · External reference
Comparison of multivariate adaptive regression splines and logistic regression in detecting SNP-SNP interactions and their application in prostate cancer
10.1007/s10038-008-0313-z · 2008 · External reference
10.1007/978-3-319-90972-1_9
10.1007/978-3-319-90972-1_9 · External reference
Learning to rank by using multivariate adaptive regression splines and conic multivariate adaptive regression splines
2020 · External reference
Multivariate adaptive regression splines model for prediction of the liquefaction-induced settlement of shallow foundations
10.1016/j.soildyn.2020.106097 · 2020 · External reference
Multivariate adaptive regression splines: A powerful method for detecting disease-risk relationship differences among subgroups
10.1002/sim.2292 · 2006 · External reference
Using data mining techniques for multi-diseases prediction modeling of hypertension and hyperlipidemia by common risk factors
10.1016/j.eswa.2010.10.086 · 2011 · External reference
Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients
2017 · External reference
Unresolved reference
2006 · External reference
Genome-enabled prediction through machine learning methods considering different levels of trait complexity
10.1002/csc2.20488 · 2021 · External reference
Superiority of artificial neural networks for a genetic classification procedure
10.4238/2015.august.19.24 · 2015 · External reference
Unresolved reference
2014 · External reference
Machine learning and statistics to qualify environments through multi-traits in Coffea arabica
10.1371/journal.pone.0245298 · 2021 · External reference
Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks
10.1016/j.heliyon.2019.e02810 · 2019 · External reference
Multivariate Adaptative regression Splines
1991 · External reference
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Multivariate adaptive regression splines and neural network models for prediction of pile drivability
10.1016/j.gsf.2014.10.003 · 2016 · External reference
Unresolved reference
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Factors affecting the accuracy of genomic selection for agricultural economic traits in maize, cattle, and pig populations
2019 · External reference
An Introduction to Statistical Learning
2021 · External reference
10.1023/a:1018054314350
10.1023/a:1018054314350 · External reference
Newer classification and regression tree techniques: Bagging and random forests for ecological prediction
10.1007/s10021-005-0054-1 · 2006 · External reference
10.1201/9780367816377-11
10.1201/9780367816377-11 · External reference
Predictive ability of Random Forests, Boosting, Support Vector Machines and Genomic Best Linear Unbiased Prediction in different scenarios of genomic evaluation
10.1071/an15538 · 2017 · External reference
No Unbiased Estimator of the Variance of K-Fold Cross-Validation
2004 · External reference
GENES - Software para análise de dados em estatística experimental e em genética quantitativa
2013 · External reference
Genes software – extended and integrated with the R, Matlab and Selegen
10.4025/actasciagron.v38i3.32629 · 2016 · External reference
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10.1146/annurev-arplant-042110-103827
10.1146/annurev-arplant-042110-103827 · External reference
Hybrid intelligent modeling schemes for heart disease classification
10.1016/j.asoc.2013.09.020 · 2014 · External reference
10.1590/0103-9016-2014-0057
10.1590/0103-9016-2014-0057 · External reference
DeepGS: Predicting phenotypes from genotypes using Deep Learning
2017 · External reference
Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species
10.3389/fpls.2020.00025 · 2020 · External reference
Prediction of phenotypic and genotypic values by BLUP/GWS and neural networks
10.1590/1983-21252018v31n301rc · 2018 · External reference
Functional models in genome-wide selection
10.1371/journal.pone.0222699 · 2019 · External reference
Sensitivity of methods for estimating breeding values using genetic markers to the number of QTL and distribution of QTL variance
10.1186/1297-9686-42-9 · 2010 · External reference
Unresolved reference
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2012 · External reference
10.1007/978-1-4612-2660-4_40
10.1007/978-1-4612-2660-4_40 · External reference
GeneSrF and varSelRF: A web-based tool and R package for gene selection and classification using random forest
10.1186/1471-2105-8-328 · 2007 · External reference
10.1007/978-3-030-31150-6
10.1007/978-3-030-31150-6 · External reference
10.1038/s41598-020-80110-w
10.1038/s41598-020-80110-w · External reference
10.1016/j.ajhg.2012.10.010
10.1016/j.ajhg.2012.10.010 · External reference
Comparing estimates of genetic variance across different relationship models
10.1016/j.tpb.2015.08.005 · 2016 · External reference
A comparison of identity-by-descent and identity-by-state matrices that are used for genetic evaluation and estimation of variance components
10.1111/jbg.12275 · 2017 · External reference
10.1038/s41437-017-0023-4
10.1038/s41437-017-0023-4 · External reference
10.1038/s41437-018-0075-0
10.1038/s41437-018-0075-0 · External reference
Accuracy of genomic predictions using different imputation error rates in aquaculture breeding programs: A simulation study
10.1016/j.aquaculture.2018.12.061 · 2019 · External reference
Accuracy of genomic BLUP when considering a genomic relationship matrix based on the number of the largest eigenvalues: A simulation study
10.1186/s12711-019-0516-0 · 2019 · External reference
10.3389/fpls.2019.01129
10.3389/fpls.2019.01129 · External reference
Bankruptcy forecasting: A hybrid approach using fuzzy c-means clustering and multivariate adaptive regression splines (MARS)
10.1016/j.eswa.2010.07.117 · 2011 · External reference
Exploration of linear modelling techniques and their combination with multivariate adaptive regression splines to predict gastro-intestinal absorption of drugs
10.1016/j.jpba.2006.06.022 · 2007 · External reference
Wheat Yield Prediction in India Using Principal Component Analysis-Multivariate Adaptive Regression Splines (PCA-MARS)
10.3390/agriengineering4020030 · 2022 · External reference
Genomic selection for growth and wood quality in Eucalyptus: Capturing the missing heritability and accelerating breeding for complex traits in forest trees
10.1111/j.1469-8137.2011.04038.x · 2012 · External reference
