🚴 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]

easyPheno: a model agnostic phenotype prediction framework

2024 BreedWheat Genomic Selection pipeline

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

Robust optimal contirbution selection problems for genetics with Python

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

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

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

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

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

A graphical software for forest genomic selection breeding

Implementations of Critical Line Algorithm in GNU Octave

Repositório com código e relatórios reprodutíveis do programa de melhoramento de mandioca da Embrapa no âmbito do NextGen Cassava (Year 6 – 2022/2023). Inclui análises de ensaios de campo, caracterização fenotípica, estimação de BLUPs, índices de seleção e integração de características de rendimento, qualidade e tolerância à seca.

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.