Genomic Prediction through Machine Learning and Neural Networks for Traits with Epistasis
Costa, W. G.
2025-03-25
Last updated: 2025-03-25
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Genomic-prediction-through-machine-learning-and-neural-networks-for-traits-with-epistasis/
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Predição Genômica através de Machine Learning e Redes Neurais para Características com Epistasia
Bem-vindo! Este repositório contém a implementação completa e recursos associados ao estudo de predição genômica utilizando técnicas avançadas de aprendizado de máquina e redes neurais artificiais, com foco especial em características influenciadas por efeitos epistáticos.
Publicação Associada
Este trabalho é parte integrante do artigo científico:
Predição Genômica através de Machine Learning e Redes Neurais
para Características com Epistasia
Autores: Weverton Gomes da Costa et al.
Periódico: Comput Struct Biotechnol J.
2022;20:5490-5499
DOI: 10.1016/j.csbj.2022.09.029
PMID: 36249559 | PMCID: PMC9547190
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:
- Código de Análise: Implementações completas dos algoritmos de ML e redes neurais
- Fluxo de Trabalho Reprodutível: Pipeline completo de análise de dados genômicos
- Dados Sintéticos: Conjuntos de dados para teste e validação
- Visualizações Interativas: Gráficos e análises exploratórias dos resultados
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
Coordenação 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/
Genomic Prediction through Machine Learning and Neural Networks for Traits with Epistasis
Welcome! This repository contains the complete implementation and associated resources for the genomic prediction study using advanced machine learning techniques and artificial neural networks, with special focus on traits influenced by epistatic effects.
Associated Publication
This work is part of the research article:
Genomic Prediction through Machine Learning and Neural Networks
for Traits with Epistasis
Authors: Weverton Gomes da Costa et al.
Journal: Comput Struct Biotechnol J.
2022;20:5490-5499
DOI: 10.1016/j.csbj.2022.09.029
PMID: 36249559 | PMCID: PMC9547190
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:
- Analysis Code: Complete implementations of ML algorithms and neural networks
- Reproducible Workflow: Complete genomic data analysis pipeline
- Synthetic Datasets: Test and validation datasets
- Interactive Visualizations: Exploratory analysis plots and results
How to Use
- Clone repository:
git clone https://github.com/wevertongomescosta/Genomic-prediction-through-machine-learning-and-neural-networks-for-traits-with-epistasis.git - Install dependencies:
renv::restore() - Run main pipeline:
Rscript scripts/main_analysis.R
Contribution
Contributions are welcome through: - Issue opening for improvement discussions - Pull request submission 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:
Weverton Gomes da Costa
Post-Doctoral Researcher - Statistics Department - UFV
weverton.costa@ufv.br
LICAE Laboratory:
licae@ufv.br | https://www.licae.ufv.br/
Full Author List
Weverton Gomes da Costa¹, Maurício de Oliveira Celeri², Ivan de Paiva
Barbosa³, Gabi Nunes Silva⁴, Camila Ferreira Azevedo³, Aluizio Borem³,
Moysés Nascimento², Cosme Damião Cruz¹
¹UFV, ²UFLA, ³Universidade de São Paulo, ⁴Universidade Federal de Minas
Gerais
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_3.6.4 knitr_1.49 rlang_1.1.5
[5] xfun_0.51 stringi_1.8.4 promises_1.3.2 jsonlite_1.9.1
[9] workflowr_1.7.1 glue_1.8.0 rprojroot_2.0.4 git2r_0.35.0
[13] htmltools_0.5.8.1 httpuv_1.6.15 sass_0.4.9 rmarkdown_2.29
[17] evaluate_1.0.3 jquerylib_0.1.4 tibble_3.2.1 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_1.6.5 Rcpp_1.0.14 pkgconfig_2.0.3
[29] rstudioapi_0.17.1 later_1.4.1 digest_0.6.37 R6_2.6.1
[33] pillar_1.10.1 magrittr_2.0.3 bslib_0.9.0 tools_4.4.3
[37] cachem_1.1.0