🚴 Kinship Adjusted Multi-Loci Best Linear Unbiased Prediction

A high-performance, ALL-in-ONE suite for quantitative genetics that unifies genome-wide association studies (GWAS) and genomic selection (GS).

Bayesian Multi-Trait Multi-Environment for genomic selection[R package] [Dev version]

2024 BreedWheat Genomic Selection pipeline

easyPheno: a model agnostic phenotype prediction framework

Prior Knowledge Dual-Path CNN

Training set determination for genomic selection.

Quantitative and population genetics analyses of populations, pools, half-sib/full-sib families, and polyploids

R package wrapping the GS3 program for genomic selection (http://genoweb.toulouse.inra.fr/~alegarra/).

Robust optimal contirbution selection problems for genetics with Python

This website is a project for analysis of the Genomic Selection for Drought Tolerance Using Genome Wide GBS and/or DART in Cassava by EMBRAPA Mandioca.

Ensemble learning for integrative prediction of genetic values with genomic variants.

Reproducible documentation: Wolfe et al. Genomic mating in outbred species: predicting cross usefulness with additive and total genetic covariance matrices

A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species

This is a public repository for relevant code from the publication of the same name.

A graphical software for forest genomic selection breeding

Implementations of Critical Line Algorithm in GNU Octave

This project documents informatics and genomic prediction to support the National Root Crop Research Institute 2021 cassava genomic selection breeding program.

O objetivo do presente projeto é propor e avaliar o uso da regularização no desempenho de RNAs em modelos de predição genômica (GP). Tal estudo visa melhorar a predição de valores genéticos genômicos (GEBV), evitando o chamado o problema de ‘overfitting’, e estimação de parâmetros genéticos. Para isso, dados simulados e reais serão utilizados.

BreedSight: an ensemble deep learning genomic prediction model for utilizing natural variation in rice breeding