🚴 Kinship Adjusted Multi-Loci Best Linear Unbiased Prediction

R front-end for TASSEL 5

Can deep learning improve genomic prediction of complex human traits? by Pau Bellot, Gustavo de los Campos, Miguel Pérez-Enciso, Genetics

Code for "Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials" (Fernandes et. al 2024)

2024 BreedWheat Genomic Selection pipeline

Trait BLUP, a Feature Selection Package for Genomic Prediction

scripts and examples

Training set determination for genomic selection.

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

Genomic prediction of amino acid traits in Arabidopsis seeds

Role of genomics on regulating rice grain metabolic variability under warmer nights: A statistical and image-based deep learning approach

This repository contains the refpop R scripts, for achieving reproducible data analysis and results, according to the FAIR principles

WISER: WhItening and successive least Squares Estimation Refinement for phenotype estimation

List of papers related to Agricultural Statistics, Plant Breeding, and Quantitative Genetics

Solves kernel ridge regression within the the mixed model framework. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available.

Gradient Boosting Regression (GBR, https://github.com/scikit-learn/scikit-learn) model for genomic predictions

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

An R package for sample size determination for genomic selection

Code and analysis pipeline for the paper "Genomic Prediction through Machine Learning and Neural Networks for Traits with Epistasis" (Comput Struct Biotechnol J, 2022).

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.