Redução dimensional de dados de expressão gênica para classificação de subtipos de câncer de mama
Resumo
O câncer de mama é mais comum em mulheres e o que mais causa mortes. A subtipagem do câncer de mama é fundamental para um tratamento eficaz, mas a alta dimensionalidade dos dados é um desafio para obtenção do diagnóstico. Para mitigar este problema, técnicas de redução dimensional podem ser aplicadas para extrair atributos mais relevantes e reduzir a dimensionalidade. Este trabalho compara estruturas de aprendizagem profunda e propõe o uso do modelo de redes siamesas como uma nova abordagem para aprimorar a classificação em subtipos de câncer de mama. Os resultados mostram que a nova abordagem melhorou em 0,05 o F1 dos subtipos mais difíceis.
Palavras-chave:
Redução dimensional, Transformação dimensional, Aprendizagem profunda, Detecção de câncer, Câncer de mama, Seleção de atributos
Referências
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Staiger, C., Cadot, S., Györffy, B., Wessels, L. F., and Klau, G. W. (2013). Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis. Frontiers in genetics, 4:289.
Taigman, Y., Yang, M., Ranzato, M., and Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Xiao, Y., Wu, J., Lin, Z., and Zhao, X. (2018). A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using rna-seq data. Computer methods and programs in biomedicine, 166:99–105.
Yersal, O. and Barutca, S. (2014). Biological subtypes of breast cancer: Prognostic and therapeutic implications. World journal of clinical oncology, 5(3):412
Ang, J. C., Mirzal, A., Haron, H., and Hamed, H. N. A. (2015). Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection. IEEE/ACM transactions on computational biology and bioinformatics, 13(5):971–989
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., and Jemal, A. (2018). Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians.
Danaee, P., Ghaeini, R., and Hendrix, D. A. (2017). A deep learning approach for cancer detection and relevant gene identification. In Pacific symposium on biocomputing
Daoud, M. and Mayo, M. (2019). A survey of neural network-based cancer prediction models from microarray data. Artificial intelligence in medicine, 97:204–214
Mendonca-Neto, R., Reis, J., Okimoto, L., Fenyö, D., Silva, C., Nakamura, F., and Nakamura, E. (2022). Classification of breast cancer subtypes: A study based on representative genes. Journal of the Brazilian Computer Society, 28(1):59–68.
Rivera-Franco, M. M. and Leon-Rodriguez, E. (2018). Delays in breast cancer detection and treatment in developing countries. Breast cancer: basic and clinical research.
Sahu, B., Mohanty, S., and Rout, S. (2019). A hybrid approach for breast cancer classification and diagnosis. EAI Endorsed Transactions on Scalable Information Systems
Staiger, C., Cadot, S., Györffy, B., Wessels, L. F., and Klau, G. W. (2013). Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis. Frontiers in genetics, 4:289.
Taigman, Y., Yang, M., Ranzato, M., and Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Xiao, Y., Wu, J., Lin, Z., and Zhao, X. (2018). A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using rna-seq data. Computer methods and programs in biomedicine, 166:99–105.
Yersal, O. and Barutca, S. (2014). Biological subtypes of breast cancer: Prognostic and therapeutic implications. World journal of clinical oncology, 5(3):412
Publicado
25/09/2023
Como Citar
ALVES, Arthur Bindá; MENDONCA-NETO, Rayol; NAKAMURA, Eduardo; NAKAMURA, Fabíola; FENYÖ, David.
Redução dimensional de dados de expressão gênica para classificação de subtipos de câncer de mama. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 17. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 25-32.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2023.234276.
