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Importance-of-markers-for-QTL-detection-by-machine-learning-methods/
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|---|---|---|---|---|
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| Rmd | 081eb18 | WevertonGomesCosta | 2022-12-22 | Start workflowr project. |
Este projeto investiga métodos computacionais inovadores para identificação de QTLs (Quantitative Trait Loci) utilizando técnicas de machine learning em estudos de associação genômica ampla (GWAS). Nosso objetivo principal é desenvolver e validar abordagens que superem as limitações dos métodos estatísticos tradicionais na detecção de marcadores moleculares associados a características complexas.
A pesquisa combina: - Algoritmos de aprendizado de máquina (Random Forest, SVM, XGBoost) - Análise de importância de marcadores genômicos - Desenvolvimento de mapas genéticos interativos - Fluxos de trabalho reprodutíveis para análise genômica
O estudo foi desenvolvido em colaboração multidisciplinar entre especialistas em genética estatística, melhoramento vegetal e inteligência computacional do LICAE/UFV e instituições parceiras.
This project investigates innovative computational methods for QTL (Quantitative Trait Loci) identification using machine learning techniques in genome-wide association studies (GWAS). Our main objective is to develop and validate approaches that overcome the limitations of traditional statistical methods in detecting molecular markers associated with complex traits.
The research combines: - Machine learning algorithms (Random Forest, SVM, XGBoost) - Genomic marker importance analysis - Interactive genetic map development - Reproducible workflows for genomic analysis
The study was developed through multidisciplinary collaboration between experts in statistical genetics, plant breeding, and computational intelligence from LICAE/UFV and partner institutions.
Weverton Gomes da Costa
Pesquisador Pós-Doutoral
Laboratório de Inteligência Computacional e Aprendizado Estatístico
(LICAE)
Departamento de Estatística - UFV
weverton.costa@ufv.br
Cosme Damião Cruz
Coordenador Científico
Instituto de Inteligência Artificial e Computacional (Idata) - UFV
cdcruz@ufv.br
Moyses Nascimento
Professor Orientador (Correspondência)
LICAE/Departamento de Estatística - UFV
moysesnascim@ufv.br
Este trabalho está licenciado sob CC BY-NC-SA 4.0. Para uso comercial ou colaborações, entre em contato com os autores.
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