Tag Archives: k-fold cross validation

Using the K-Fold Cross-Validation Statistics to Understand the Predictive Power of your Data in SVS

SVS 8

Using the K-Fold Cross-Validation Statistics to Understand the Predictive Power of your Data in SVS In cross-validation, a set of data is divided into two parts, the “training set” and the “validation set”. A model for predicting a phenotype from genotypic data and (usually) some fixed effect parameters is “trained” using the training set—that is, the best value(s) of the… Read more »

Optimized Breeding Selection via Genomic Prediction

What is Genomic Prediction? Genomic prediction is an algorithm widely used to improve desirable phenotypic traits in agriculture. For example, the cattle industry uses genomic prediction to improve beef quality and palatability as well as improve dairy production (1,2). By using genomic prediction, researchers can minimize multiple expenses in breeding industries as well as diminish the need for performing cumbersome… Read more »

Cross-Validation for Genomic Prediction in SVS

The SNP and Variation Suite (SVS) software currently supports three methods for genomic prediction: Genomic Best Linear Unbiased Predictors (GBLUP), Bayes C and Bayes C-pi. We have discussed these methods extensively in previous blogs and webcast events.  Although there are extensive applications for these methods, they are primarily used for trait selection in agricultural genetics. Each method can be used… Read more »