Pipeline Reprodutível e Responsável de Ciência de Dados para Apoio à Detecção Precoce do Câncer de Pulmão
Resumo
Este artigo apresenta um pipeline reprodutível de engenharia de dados para Ciência de Dados orientada à saúde pública em oncologia pulmonar. O fluxo estrutura fontes biomédicas públicas por meio de proveniência, validação de integridade, controle de qualidade, extração semântica de rótulos, alinhamento matriz–metadados, particionamento anti-vazamento e artefatos versionados. Na execução auditada, GSE43458 e GSE32863 foram consolidadas com rótulos binários completos e sem amostras não classificadas, o que pode contribuir para reduzir barreiras técnicas à pesquisa reprodutível ao documentar proveniência, regras de rotulagem, verificações de qualidade e artefatos versionados.
Palavras-chave:
Ciência de Dados em Saúde Pública, Reprodutibilidade, Oncologia Pulmonar
Referências
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Kabbout, M., Garcia, M. M., Fujimoto, J., Liu, D. D., Woods, D., Chow, C.-W. et al. (2013). “ETS2 mediated tumor suppressive function and MET oncogene inhibition in human non-small cell lung cancer”. Clinical Cancer Research, 19(13):3383–3395.
Selamat, S. A., Chung, B. S., Girard, L., Zhang, W., Zhang, Y., Campan, M. et al. (2012). “Genome-scale analysis of DNA methylation in lung adenocarcinoma and integration with mRNA expression”. Genome Research, 22(7):1197–1211.
Wang, H., Zhao, W., Zhang, Y., Li, M. and Wang, P. (2024). “Depletion-assisted multiplexed cell-free RNA sequencing reveals distinct human and microbial signatures in plasma versus extracellular vesicles”. Clinical and Translational Medicine, 14(7):e1760.
Cancer Genome Atlas Research Network (2014). “Comprehensive molecular profiling of lung adenocarcinoma”. Nature, 511(7511):543–550.
Collins, G. S., Reitsma, J. B., Altman, D. G. and Moons, K. G. M. (2015). “Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement”. Annals of Internal Medicine, 162(1):55–63.
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Steyerberg, E. W. and Vergouwe, Y. (2014). “Towards better clinical prediction models: seven steps for development and an ABCD for validation”. European Heart Journal, 35(29):1925–1931.
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J. J., Appleton, G., Axton, M., Baak, A. et al. (2016). “The FAIR Guiding Principles for scientific data management and stewardship”. Scientific Data, 3:160018.
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Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S. et al. (2019). “PROBAST: A tool to assess the risk of bias and applicability of prediction model studies”. Annals of Internal Medicine, 170(1):51–58.
Best, M. G., Sol, N., In ’t Veld, S. G. J. G., Vancura, A., Muller, M., Niemeijer, A.-L. N. et al. (2017). “Swarm intelligence-enhanced detection of non-small-cell lung cancer using tumor-educated platelets”. Cancer Cell, 32(2):238–252.e9.
Kabbout, M., Garcia, M. M., Fujimoto, J., Liu, D. D., Woods, D., Chow, C.-W. et al. (2013). “ETS2 mediated tumor suppressive function and MET oncogene inhibition in human non-small cell lung cancer”. Clinical Cancer Research, 19(13):3383–3395.
Selamat, S. A., Chung, B. S., Girard, L., Zhang, W., Zhang, Y., Campan, M. et al. (2012). “Genome-scale analysis of DNA methylation in lung adenocarcinoma and integration with mRNA expression”. Genome Research, 22(7):1197–1211.
Wang, H., Zhao, W., Zhang, Y., Li, M. and Wang, P. (2024). “Depletion-assisted multiplexed cell-free RNA sequencing reveals distinct human and microbial signatures in plasma versus extracellular vesicles”. Clinical and Translational Medicine, 14(7):e1760.
Cancer Genome Atlas Research Network (2014). “Comprehensive molecular profiling of lung adenocarcinoma”. Nature, 511(7511):543–550.
Collins, G. S., Reitsma, J. B., Altman, D. G. and Moons, K. G. M. (2015). “Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement”. Annals of Internal Medicine, 162(1):55–63.
Edgar, R., Domrachev, M. and Lash, A. E. (2002). “Gene Expression Omnibus: NCBI gene expression and hybridization array data repository”. Nucleic Acids Research, 30(1):207–210.
Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning. Springer, New York, 2nd edition.
Siegel, R. L., Giaquinto, A. N. and Jemal, A. (2024). “Cancer statistics, 2024”. CA: A Cancer Journal for Clinicians, 74(1):12–49.
Rudin, C. (2019). “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead”. Nature Machine Intelligence, 1:206–215.
Steyerberg, E. W. and Vergouwe, Y. (2014). “Towards better clinical prediction models: seven steps for development and an ABCD for validation”. European Heart Journal, 35(29):1925–1931.
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J. J., Appleton, G., Axton, M., Baak, A. et al. (2016). “The FAIR Guiding Principles for scientific data management and stewardship”. Scientific Data, 3:160018.
World Health Organization (2024). “Global cancer burden growing, amidst mounting need for services”. WHO news release, 1 February 2024.
Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S. et al. (2019). “PROBAST: A tool to assess the risk of bias and applicability of prediction model studies”. Annals of Internal Medicine, 170(1):51–58.
Publicado
08/09/2026
Como Citar
OLINTO, Douglas L. P.; BARRETTA, Luciana O.; DIAS, Elisângela S..
Pipeline Reprodutível e Responsável de Ciência de Dados para Apoio à Detecção Precoce do Câncer de Pulmão. In: DATA SCIENCE FOR SOCIAL GOOD BRAZILIAN WORKSHOP (DS4SG) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 41. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 483-492.
DOI: https://doi.org/10.5753/sbbd_estendido.2026.249681.
