Optimized Neural Networks for Breast Cancer Classification Using Gene Expression Data
Resumo
Este estudo tem como objetivo desenvolver e avaliar redes neurais otimizadas, incluindo Perceptrons Multicamadas (MLP) e Redes Neurais Convolucionais (CNN), empregando técnicas de aprendizagem profunda para classificar subtipos de câncer de mama, com base em dados de expressão gênica. Ao implementar diferentes arquiteturas de redes neurais e estratégias de otimização, este trabalho busca determinar a acurácia e a eficiência desses métodos de classificação. Os dados são provenientes do repositório Atlas do Genoma do Câncer (TCGA) e passam por pré-processamento, incluindo redução de dimensionalidade, a fim de prepará-los para análise. A contribuição é aprimorar as ferramentas de diagnóstico, bem como avaliar o desempenho preditivo das abordagens. A comparação dos resultados das redes apresenta um caminho promissor para aumentar a precisão dos diagnósticos médicos e personalizar as estratégias de tratamento do câncer de mama.Referências
Aggarwal, C. C. (2015). Data Classification. CRC Press, Florida - USA.
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. CoRR, abs/1907.10902.
Bishop, C. M. (2006). Pattern recognition and machine learning, volume 2. Springer.
Chollet, F. (2021). Deep Learning with Python. New York: Manning Publication, second edition.
Dablain, D., Jacobson, K. N., Bellinger, C., Roberts, M., and Chawla, N. V. (2024). Understanding cnn fragility when learning with imbalanced data. Machine Learning, 113(7):4785–4810.
Iparraguirre-Villanueva, O., Epifanía-Huerta, A., Torres-Ceclén, C., Ruiz-Alvarado, J., and Cabanillas-Carbonell, M. (2023). Breast cancer prediction using machine learning models. International Journal of Advanced Computer Science and Applications, 14(2):610–620.
Johnson, R. A. and Wichern, D. W. (2007). Applied Multivariate Statistical Analysis, volume 6. Pearson Education.
Kreyszig, E. (2010). Advanced Engineering Mathematics. John Wiley & Sons, New York, 10 edition.
LeCun, Y., Bengio, Y., et al. (1995). Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10):1995.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324.
Lopez-Garcia, G., Jerez, J. M., Franco, L., and Veredas, F. J. (2020). Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data. PloS one, 15(3):e0230536.
Ministério da Saúde (2024). Câncer de mama. [link]. Accessed: 2024-08-26.
Rabiei, R., M., A. S., Sohrabei, S., Esmaeili, M., and Atashi, A. (2022). Prediction of breast cancer using machine learning approaches. J Biomed Phys Eng, 12(3):297–308.
Rencher, A. (2002). Methods of Multivariate Analysis. Wiley series in probability and mathematical statistics. Wiley.
Sait, A. R. W. and Nagaraj, R. (2024). An enhanced lightgbm-based breast cancer detection technique using mammography images. Diagnostics, 14(2):227.
Tewari, Y., Ujjwal, E., and Kumar, L. (2022). Breast cancer classification using machine learning. In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE).
Timm, N. (2002). Applied Multivariate Analysis. Springer Texts in Statistics. Springer New York.
Turgut, S., Dağtekin, M., and Ensari, T. (2018). Microarray breast cancer data classification using machine learning methods. In 2018 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT).
World Health Organization (2023). Who launches new roadmap on breast cancer. [link]. Accessed: 2024-08-26.
Wu, J., Chen, X.-Y., Zhang, H., Xiong, L.-D., Lei, H., and Deng, S.-H. (2019). Hyperparameter optimization for machine learning models based on bayesian optimizationb. Journal of Electronic Science and Technology, 17(1):26–40.
Wu, J. and Hicks, C. (2021). Breast cancer type classification using machine learning. Journal of Personalized Medicine, 11(61).
Yang, L. and Shami, A. (2020). On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415:295–316.
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. CoRR, abs/1907.10902.
Bishop, C. M. (2006). Pattern recognition and machine learning, volume 2. Springer.
Chollet, F. (2021). Deep Learning with Python. New York: Manning Publication, second edition.
Dablain, D., Jacobson, K. N., Bellinger, C., Roberts, M., and Chawla, N. V. (2024). Understanding cnn fragility when learning with imbalanced data. Machine Learning, 113(7):4785–4810.
Iparraguirre-Villanueva, O., Epifanía-Huerta, A., Torres-Ceclén, C., Ruiz-Alvarado, J., and Cabanillas-Carbonell, M. (2023). Breast cancer prediction using machine learning models. International Journal of Advanced Computer Science and Applications, 14(2):610–620.
Johnson, R. A. and Wichern, D. W. (2007). Applied Multivariate Statistical Analysis, volume 6. Pearson Education.
Kreyszig, E. (2010). Advanced Engineering Mathematics. John Wiley & Sons, New York, 10 edition.
LeCun, Y., Bengio, Y., et al. (1995). Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10):1995.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324.
Lopez-Garcia, G., Jerez, J. M., Franco, L., and Veredas, F. J. (2020). Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data. PloS one, 15(3):e0230536.
Ministério da Saúde (2024). Câncer de mama. [link]. Accessed: 2024-08-26.
Rabiei, R., M., A. S., Sohrabei, S., Esmaeili, M., and Atashi, A. (2022). Prediction of breast cancer using machine learning approaches. J Biomed Phys Eng, 12(3):297–308.
Rencher, A. (2002). Methods of Multivariate Analysis. Wiley series in probability and mathematical statistics. Wiley.
Sait, A. R. W. and Nagaraj, R. (2024). An enhanced lightgbm-based breast cancer detection technique using mammography images. Diagnostics, 14(2):227.
Tewari, Y., Ujjwal, E., and Kumar, L. (2022). Breast cancer classification using machine learning. In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE).
Timm, N. (2002). Applied Multivariate Analysis. Springer Texts in Statistics. Springer New York.
Turgut, S., Dağtekin, M., and Ensari, T. (2018). Microarray breast cancer data classification using machine learning methods. In 2018 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT).
World Health Organization (2023). Who launches new roadmap on breast cancer. [link]. Accessed: 2024-08-26.
Wu, J., Chen, X.-Y., Zhang, H., Xiong, L.-D., Lei, H., and Deng, S.-H. (2019). Hyperparameter optimization for machine learning models based on bayesian optimizationb. Journal of Electronic Science and Technology, 17(1):26–40.
Wu, J. and Hicks, C. (2021). Breast cancer type classification using machine learning. Journal of Personalized Medicine, 11(61).
Yang, L. and Shami, A. (2020). On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415:295–316.
Publicado
02/12/2024
Como Citar
VALENTIN, Ana Beatriz Miranda; BRESSAN, Glaucia Maria; CANUTO JUNIOR, Leonardo; LIZZI, Elisângela Ap. da Silva.
Optimized Neural Networks for Breast Cancer Classification Using Gene Expression Data. In: SIMPÓSIO BRASILEIRO DE BIOINFORMÁTICA (BSB), 17. , 2024, Vitória/ES.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 36-46.
ISSN 2316-1248.
DOI: https://doi.org/10.5753/bsb.2024.245194.
