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Genomic-prediction-through-machine-learning-and-neural-networks-for-traits-with-epistasis/
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Este projeto investiga métodos avançados de predição genômica utilizando técnicas de machine learning e redes neurais artificiais, com foco especial em características influenciadas por efeitos epistáticos. Nosso objetivo é desenvolver modelos preditivos que capturem eficientemente interações genéticas complexas, superando as limitações das abordagens estatísticas tradicionais.
O estudo compara o desempenho de diferentes algoritmos de aprendizado de máquina com métodos convencionais de melhoramento genético, utilizando dados genômicos de alta dimensionalidade. Todo o processo analítico é implementado em ambiente reprodutível, integrando ferramentas de bioinformática e ciência de dados.
Publicação Associada
Este script é parte integrante do artigo:
Genomic prediction through machine learning and neural networks for
traits with epistasis
Autores: Weverton Gomes da Costa, Maurício de Oliveira
Celeri, Ivan de Paiva Barbosa, Gabi Nunes Silva, Camila Ferreira
Azevedo, Aluizio Borem, Moysés Nascimento, Cosme Damião Cruz
Periódico: Comput Struct Biotechnol J.
2022;20:5490-5499
DOI: 10.1016/j.csbj.2022.09.029
PMID: 36249559 | PMCID: PMC9547190
Este projeto foi desenvolvido no âmbito das pesquisas do LICAE1.
This project investigates advanced genomic prediction methods using machine learning techniques and artificial neural networks, with special focus on traits influenced by epistatic effects. Our goal is to develop predictive models that efficiently capture complex genetic interactions, overcoming the limitations of traditional statistical approaches.
The study compares the performance of different machine learning algorithms with conventional genetic improvement methods using high-dimensional genomic data. The entire analytical process is implemented in a reproducible environment, integrating bioinformatics tools and data science.
Associated Publication
This script is part of the research article:
Genomic prediction through machine learning and neural networks for
traits with epistasis
Authors: Weverton Gomes da Costa, Maurício de Oliveira
Celeri, Ivan de Paiva Barbosa, Gabi Nunes Silva, Camila Ferreira
Azevedo, Aluizio Borem, Moysés Nascimento, Cosme Damião Cruz
Journal: Comput Struct Biotechnol J.
2022;20:5490-5499
DOI: 10.1016/j.csbj.2022.09.029
PMID: 36249559 | PMCID: PMC9547190
This project was developed as part of the research activities at LICAE.
Author
Costa, W. G.2
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 Laboratório de Inteligência Computacional e Aprendizado Estatístico - Universidade Federal de Viçosa↩︎
Weverton Gomes da Costa, Post-Doctoral Researcher, Department of Statistics at the Federal University of Viçosa, weverton.costa@ufv.br↩︎