Importance of Markers for QTL Detection by Machine Learning Methods

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Last updated: 2025-07-29

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Knit directory: Importance-of-markers-for-QTL-detection-by-machine-learning-methods/

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Português

Detecção de QTLs por Métodos de Machine Learning

GWAS ML Framework

Bem-vindo ao repositório oficial deste projeto de pesquisa, que visa aprimorar a identificação de marcadores moleculares para detecção de QTLs utilizando técnicas de machine learning. A seguir, você encontrará uma visão geral dos principais objetivos, recursos e instruções para utilização deste repositório.

Objetivos do Projeto

• Desenvolver um Pipeline Computacional: Implementar um fluxo completo de análises GWAS com métodos de ML.
• Comparar Desempenho de Algoritmos: Avaliar e comparar algoritmos (MLP, RBF, Decision Tree, Bagging, Random Forest, Boosting e MARS) na identificação de QTLs.
• Propor Métricas de Importância: Estabelecer métricas que possibilitem avaliar a relevância dos marcadores genômicos.
• Visualizações Interativas: Criar ferramentas que permitam a exploração e interpretação dos dados e dos resultados.

Navegação Rápida

• :world_map: Mapa Genético - Visualização da ligação cromossômica
• :bar_chart: Importância de Marcadores - Análise comparativa dos algoritmos
• :page_facing_up: Sobre - Detalhes metodológicos e institucionais
• :balance_scale: Licença - Termos de uso e distribuição

Sobre o LICAE

Este projeto foi desenvolvido no âmbito do Laboratório de Inteligência Computacional e Aprendizado Estatístico (LICAE) da Universidade Federal de Viçosa (UFV), especializado em aplicações avançadas de inteligência computacional para desafios genômicos.

Recursos Disponíveis

• Código de Análise: Implementações completas dos algoritmos de machine learning.
• Workflow Reprodutível: Pipeline abrangente para análise de dados genômicos.
• Dados Sintéticos: Conjuntos de dados para teste e validação.
• Visualizações Interativas: Ferramentas para exploração dos resultados.

Como Utilizar

1. Clonar o Repositório:
   git clone https://github.com/WevertonGomesCosta/Importance-of-markers-for-QTL-detection-by-machine-learning-methods.git
2. Instalar Dependências:
   renv::restore()
3. Executar o Pipeline Principal:
   Rscript scripts/main_analysis.R

Contribuição

Contribuições são bem-vindas!
- Abra issues para sugerir melhorias;
- Submeta pull requests com correções e novas ideias;
- Colabore com sugestões de extensões metodológicas.

Licença

Este trabalho está licenciado sob CC BY-NC-SA 4.0.
Para uso comercial ou modificações significativas, entre em contato com os autores.

Contato

Coordenador do Projeto:
Weverton Gomes da Costa
Pós-Doutorando - Departamento de Estatística - UFV
weverton.costa@ufv.br

Laboratório LICAE:
licae@ufv.br | https://www.licae.ufv.br/

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English

QTL Detection Through Machine Learning Methods

GWAS ML Framework

Welcome to the official repository for this research project, which aims to improve the identification of molecular markers for QTL detection using machine learning techniques. Below you will find an overview of the project’s main objectives, key features, and instructions for using this repository.

Project Objectives

• Develop a Computational Pipeline: Implement a comprehensive GWAS analysis workflow using ML methods.
• Compare Algorithm Performance: Evaluate and compare algorithms (MLP, RBF, Decision Tree, Bagging, Random Forest, Boosting, and MARS) in detecting QTLs.
• Propose Marker Importance Metrics: Establish metrics to assess the relevance of genomic markers.
• Interactive Visualizations: Create tools for exploring and interpreting genomic data and results.

Quick Navigation

• :world_map: Genetic Map - Chromosomal linkage visualization
• :bar_chart: Marker Importance - Comparative algorithm analysis
• :page_facing_up: About - Methodological and institutional details
• :balance_scale: License - Terms of use and distribution

About LICAE

This project was developed at the Computational Intelligence and Statistical Learning Laboratory (LICAE) of the Federal University of Viçosa (UFV), specializing in advanced computational intelligence applications to complex genomic problems.

Available Resources

• Analysis Code: Complete implementations of machine learning algorithms.
• Reproducible Workflow: Comprehensive genomic data analysis pipeline.
• Synthetic Datasets: Datasets for testing and validation.
• Interactive Visualizations: Tools for exploratory data analysis and results interpretation.

How to Use

1. Clone the Repository:
   git clone https://github.com/WevertonGomesCosta/Importance-of-markers-for-QTL-detection-by-machine-learning-methods.git
2. Install Dependencies:
   renv::restore()
3. Run the Main Pipeline:
   Rscript scripts/main_analysis.R

Contribution

Contributions are welcome!
- Open issues to suggest improvements;
- Submit pull requests with fixes and new ideas;
- Share 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:
Weverton Gomes da Costa
Post-Doctoral Researcher - Statistics Department - UFV
weverton.costa@ufv.br

LICAE Laboratory:
licae@ufv.br | https://www.licae.ufv.br/

Session information

sessionInfo()

R version 4.4.1 (2024-06-14 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_1.0.4    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_4.4.1    fs_1.6.6          Rcpp_1.1.0        pkgconfig_2.0.3  
[29] rstudioapi_0.17.1 later_1.4.2       digest_0.6.37     R6_2.6.1         
[33] pillar_1.11.0     magrittr_2.0.3    bslib_0.9.0       tools_4.4.1      
[37] cachem_1.1.0