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English Information

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Authors
# Name
1 Arthur Alves(aba@icomp.ufam.edu.br)
2 Rayol Mendonca-Neto(rayol@icomp.ufam.edu.br)
3 David Fenyo(david@fenyolab.org)
4 Eduardo Nakamura(nakamura@icomp.ufam.edu.br)
5 Fabíola Nakamura(fabiola@icomp.ufam.edu.br)

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Reference
# Reference
1 Adem, K. (2020). Diagnosis of breast cancer with stacked autoencoder and subspace knn. Physica A: Statistical Mechanics and its Applications, 551:124591
2 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
3 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.
4 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
5 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
6 Mendonca-Neto, R., Reis, J., Okimoto, L., Feny¨o, 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.
7 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.
8 Sahu, B., Mohanty, S., and Rout, S. (2019). A hybrid approach for breast cancer classification and diagnosis. EAI Endorsed Transactions on Scalable Information Systems
9 Staiger, C., Cadot, S., Gy¨orffy, 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.
10 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).
11 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.
12 Yersal, O. and Barutca, S. (2014). Biological subtypes of breast cancer: Prognostic and therapeutic implications. World journal of clinical oncology, 5(3):412