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Machine Learning e Redes Neurais no Melhoramento Genético do Cafeeiro
Bem-vindo! Este repositório reúne código, dados e relatórios associados ao estudo de seleção genômica ampla (GWS) em Coffea arabica, empregando métodos de machine learning e redes neurais artificiais para seleção de genótipos e detecção de SNPs informativos.
Publicação Associada
Este trabalho faz parte do Projeto de Pesquisa:
- Processo: BPD-00922-22
- Chamada: Edital 017/2022 – Programa de Apoio à Fixação de Jovens
Doutores
- Período: 01/04/2023 a 28/02/2025
Sobre o LICAE
Este projeto foi desenvolvido no âmbito das pesquisas do Laboratório de Inteligência Computacional e Aprendizado Estatístico (LICAE) da Universidade Federal de Viçosa (UFV), especializado em aplicações avançadas de inteligência computacional em problemas genômicos complexos.
Recursos Disponíveis
- Código de Análise: Scripts R/Python para
pré-processamento, modelagem e avaliação.
- Dados: Genótipos reais (195 indivíduos, 21 211
SNPs) e simulados (1 000 indivíduos, 4 010 SNPs).
- Notebooks: Exemplos interativos de EDA, GWS e
GWAS.
- Visualizações: Gráficos de acurácia (R²), RMSE e ranking de genótipos.
Como Utilizar
- Clone o repositório:
git clone https://github.com/wevertongomescosta/Genomic-prediction-through-machine-learning-and-neural-networks-for-traits-with-epistasis.git - Instale as dependências:
renv::restore() - Execute o pipeline principal:
Rscript scripts/main_analysis.R
Contribuição
Contribuições são bem-vindas mediante: - Abertura de issues para discussão de melhorias - Submissão de pull requests para correções críticas - Sugestões de extensões metodológicas
Licença
Este trabalho está licenciado sob CC BY-NC-SA 4.0. Para uso comercial ou modificações significativas, por favor contate os autores.
Contato
Coordenador
Moyses Nascimento
Professor Adjunto - Departamento de Estatística - UFV moysesnascim@ufv.br
Bolsista
Weverton Gomes da Costa
Pós-Doutorando - Departamento de Estatística - UFV
weverton.costa@ufv.br
Laboratório LICAE:
licae@ufv.br | https://www.licae.ufv.br/
Machine Learning and Neural Networks in Coffee Genetic Breeding
Welcome! This repository gathers code, data, and reports associated with the Genome Wide Selection (GWS) study in Coffea arabica, employing machine learning and artificial neural network methods for genotype selection and informative SNP detection.
Associated Project
This work is part of the Research Project:
- Process: BPD-00922-22
- Call: Notice 017/2022 – Program to Support the Retention of Young
Postdocs in Brazil
- Period: April 1, 2023 to February 28, 2025
About LICAE
This project was developed within the research activities of the Computational Intelligence and Statistical Learning Laboratory (LICAE) at the Federal University of Viçosa (UFV), specialized in advanced computational intelligence applications to complex genomic problems.
Available Resources
- Analysis Code: R and Python scripts for
preprocessing, modeling, and evaluation.
- Data: Real genotypes (195 individuals, 21,211 SNPs)
and simulated (1,000 individuals, 4,010 SNPs).
- Notebooks: Interactive examples of EDA, GWS, and
GWAS.
- Visualizations: Plots of accuracy (R²), RMSE, and genotype rankings.
How to Use
Clone the repository:
Install dependencies:
Run the main pipeline:
Contribution
Contributions are welcome via:
- Opening issues for improvement discussions
- Submitting pull requests for critical fixes
- Suggestions for methodological extensions
License
This work is licensed under CC BY-NC-SA 4.0. For commercial use or significant modifications, please contact the authors.
Contact
Project Coordinator
Moyses Nascimento
Associate Professor, Department of Statistics, UFV
moysesnascim@ufv.br
Research Fellow
Weverton Gomes da Costa
Post-Doctoral Researcher, Department of Statistics, UFV
weverton.costa@ufv.br
LICAE Laboratory
licae@ufv.br | https://www.licae.ufv.br/
R version 4.4.3 (2025-02-28 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
locale:
[1] LC_COLLATE=Portuguese_Brazil.utf8 LC_CTYPE=Portuguese_Brazil.utf8
[3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C
[5] LC_TIME=Portuguese_Brazil.utf8
time zone: America/Sao_Paulo
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] vctrs_0.6.5 cli_3.6.5 knitr_1.50 rlang_1.1.6
[5] xfun_0.52 stringi_1.8.7 promises_1.3.3 jsonlite_2.0.0
[9] workflowr_1.7.1 glue_1.8.0 rprojroot_2.0.4 git2r_0.36.2
[13] htmltools_0.5.8.1 httpuv_1.6.16 sass_0.4.10 rmarkdown_2.29
[17] evaluate_1.0.3 jquerylib_0.1.4 tibble_3.3.0 fastmap_1.2.0
[21] yaml_2.3.10 lifecycle_1.0.4 whisker_0.4.1 stringr_1.5.1
[25] compiler_4.4.3 fs_1.6.6 Rcpp_1.0.14 pkgconfig_2.0.3
[29] rstudioapi_0.17.1 later_1.4.2 digest_0.6.37 R6_2.6.1
[33] pillar_1.10.2 magrittr_2.0.3 bslib_0.9.0 tools_4.4.3
[37] cachem_1.1.0