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Rmd	081eb18	WevertonGomesCosta	2022-12-22	Start workflowr project.

Detecção de QTLs através de Métodos de Machine Learning

GWAS ML Framework

Bem-vindo ao repositório oficial do projeto de pesquisa sobre identificação de marcadores moleculares para detecção de QTLs utilizando métodos de aprendizado de máquina.

Objetivos Principais

Desenvolver pipeline computacional para análise GWAS com ML
Comparar desempenho de algoritmos na detecção de QTLs
Propor métricas de importância de marcadores genômicos
Criar ferramentas interativas de visualização genética

Recursos Chave

Análises Reprodutíveis: Fluxo completo de dados genômicos
Comparativo de Algoritmos: Random Forest, XGBoost, SVM
Mapas Genéticos Interativos: Visualização de ligação cromossômica
Benchmarking: Comparação com métodos estatísticos tradicionais

QTL Detection Through Machine Learning Methods

GWAS ML Framework

Welcome to the official repository for the research project on molecular marker identification for QTL detection using machine learning methods.

Main Objectives

Develop computational pipeline for GWAS analysis with ML
Compare algorithm performance in QTL detection
Propose genomic marker importance metrics
Create interactive genetic visualization tools

Key Features

Reproducible Analyses: Complete genomic data workflow
Algorithm Comparison: Random Forest, XGBoost, SVM
Interactive Genetic Maps: Chromosomal linkage visualization
Benchmarking: Comparison with traditional statistical methods

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

Este repositório contém: 1. Código de Análise: Implementações completas dos algoritmos de ML 2. Fluxo de Trabalho Reprodutível: Pipeline completo de análise de dados genômicos 3. Dados Sintéticos: Conjuntos de dados para teste e validação 4. Visualizações Interativas: Gráficos e análises exploratórias dos resultados

About LICAE

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

Available Resources

This repository contains: 1. Analysis Code: Complete implementations of ML algorithms 2. Reproducible Workflow: Complete genomic data analysis pipeline 3. Synthetic Datasets: Test and validation datasets 4. Interactive Visualizations: Exploratory analysis plots and results

Como Utilizar

Clone o repositório: git clone https://github.com/WevertonGomesCosta/Importance-of-markers-for-QTL-detection-by-machine-learning-methods.git
Instale as dependências: renv::restore()
Execute o pipeline principal: Rscript scripts/analise_principal.R

How to Use

Clone repository: git clone https://github.com/WevertonGomesCosta/Importance-of-markers-for-QTL-detection-by-machine-learning-methods.git
Install dependencies: renv::restore()
Run main pipeline: 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

Contribution

Contributions are welcome through: - Issue opening for improvement discussions - Pull request submission for critical fixes - Suggestions for methodological extensions

Licença

Este trabalho está licenciado sob CC BY-NC-SA 4.0. Para uso comercial ou modificações significativas, por favor contate os autores.

License

This work is licensed under CC BY-NC-SA 4.0. For commercial use or significant modifications, please contact the authors.

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/

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/

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


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Importance of markers for QTL detection by machine learning methods

Costa, W. G.

2025-03-25