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Rmd be23fb6 WevertonGomesCosta 2025-07-09 Start workflowr project.

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.

Pipeline de Análise
Pipeline de Análise

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

Desenvolvido no Laboratório de Inteligência Computacional e Aprendizado Estatístico (LICAE) da UFV, especialista em aplicações de IA em genômica vegetal.

Recursos Disponíveis

  1. Código de Análise: Scripts R/Python para pré-processamento, modelagem e avaliação.
  2. Dados: Genótipos reais (195 indivíduos, 21 211 SNPs) e simulados (1 000 indivíduos, 4 010 SNPs).
  3. Notebooks: Exemplos interativos de EDA, GWS e GWAS.
  4. Visualizações: Gráficos de acurácia (R²), RMSE e ranking de genótipos.
Arquitetura do Modelo
Arquitetura do Modelo

Como Utilizar

git clone https://github.com/wevertongomescosta/Machine-learning-e-redes-neurais-no-melhoramento-genetico-do-cafeeiro.git
cd Machine-learning-e-redes-neurais-no-melhoramento-genetico-do-cafeeiro
# R
Rscript -e "install.packages('renv'); renv::restore()"
Rscript scripts/main_analysis.R
# Python (se houver)
pip install -r requirements.txt
python src/train.py

Contribuição

Contribuições são bem-vindas via:
- Issues para discussão de bugs e novas funcionalidades
- Pull requests com correções ou extensões
- Sugestões de metodologias ou visualizações

Consulte o CONTRIBUTING.md para detalhes.

Licença

Este projeto está sob a licença MIT. Veja LICENSE para detalhes.

Contato

Coordenador
Moyses Nascimento

Bolsista
Weverton Gomes da Costa

Machine Learning and Neural Networks for Genomic Selection in Coffee

Welcome! This repository contains all code, data, and documentation for a genomic selection (GWS) study in Coffea arabica, applying machine learning and artificial neural networks to select superior genotypes and discover informative SNPs.

Associated Publication

This work is part of the research project:

  • Processo: BPD-00922-22
  • Chamada: Edital 017/2022 – Programa de Apoio à Fixação de Jovens Doutores
  • Período: 01/04/2023 a 28/02/2025

About LICAE

Developed at the Computational Intelligence and Statistical Learning Laboratory (LICAE) at UFV, specializing in AI applications for plant genomics.

Available Resources

  1. Analysis Code: R/Python scripts for preprocessing, modeling and evaluation.
  2. Datasets: Real genotypes (195 samples, 21,211 SNPs) and simulated population (1,000 samples, 4,010 SNPs).
  3. Notebooks: Interactive EDA, GWS and GWAS tutorials.
  4. Visualizations: Plots of predictive accuracy (R²), RMSE and genotype rankings.

How to Use

git clone https://github.com/wevertongomescosta/Machine-learning-e-redes-neurais-no-melhoramento-genetico-do-cafeeiro.git
cd Machine-learning-e-redes-neurais-no-melhoramento-genetico-do-cafeeiro
# R
Rscript -e "install.packages('renv'); renv::restore()"
Rscript scripts/main_analysis.R
# Python (if applicable)
pip install -r requirements.txt
python src/train.py

Contribution

Contributions welcome via issues and pull requests—see CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License. See LICENSE for details.

Contact

Project Coordinator
Moyses Nascimento

Research Fellow
Weverton Gomes da Costa


sessionInfo()
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