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Knit directory: Machine-learning-e-redes-neurais-artificiais-no-melhoramento-genetico-do-cafeeiro/

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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 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 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:

git clone https://github.com/wevertongomescosta/coffee-gws.git

Install dependencies:
```
renv::restore()
```
Run the main pipeline:
```
Rscript scripts/main_analysis.R
```

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/

sessionInfo()

R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26200)

Matrix products: default
  LAPACK version 3.12.1

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_0.53         stringi_1.8.7     promises_1.3.3    jsonlite_2.0.0   
 [9] workflowr_1.7.2   glue_1.8.0        rprojroot_2.1.1   git2r_0.36.2     
[13] htmltools_0.5.8.1 httpuv_1.6.16     sass_0.4.10       rmarkdown_2.29   
[17] evaluate_1.0.5    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_1.5.2    
[25] compiler_4.5.1    fs_1.6.6          Rcpp_1.1.0        pkgconfig_2.0.3  
[29] rstudioapi_0.17.1 later_1.4.4       digest_0.6.37     R6_2.6.1         
[33] pillar_1.11.1     magrittr_2.0.4    bslib_0.9.0       tools_4.5.1      
[37] cachem_1.1.0

Weverton Gomes da Costa, Pós-Doutorando, Departamento de Estatística - Universidade Federal de Viçosa, wevertonufv@gmail.com ↩︎

Machine Learning e Redes Neurais no Melhoramento Genético do Cafeeiro

Costa, W. G.1

2025-11-05

Machine Learning e Redes Neurais no Melhoramento Genético do Cafeeiro

Publicação Associada

Sobre o LICAE

Recursos Disponíveis

Como Utilizar

Contribuição

Licença

Contato

Machine Learning and Neural Networks in Coffee Genetic Breeding

Associated Project

About LICAE

Available Resources

How to Use

Contribution

License

Contact

Costa, W. G.¹