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English Information

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Authors
# Name
1 Renan Andrades(rsandrades@inf.ufrgs.br)
2 Mariana Recamonde Mendoza(mrmendoza@inf.ufrgs.br)

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Reference
# Reference
1 Andrades, R. and Recamonde-Mendoza, M. (2022). Machine learning methods for pre- diction of cancer driver genes: a survey paper. Briefings in Bioinformatics, 23(3). bbac062.
2 Huang, J. K., Carlin, D. E., Yu, M. K., Zhang, W., Kreisberg, J. F., Tamayo, P., and Ideker, T. (2018). Systematic evaluation of molecular networks for discovery of disease genes. Cell Systems, 6(4):484–495.
3 Jung, S., Wang, S., and Lee, D. (2024). CancerGATE: Prediction of cancer-driver genes using graph attention autoencoders. Computers in Biology and Medicine, 176:108568.
4 Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll´ar, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision, pages 2980–2988.
5 Ostroverkhova, D., Przytycka, T. M., and Panchenko, A. R. (2023). Cancer driver muta- tions: predictions and reality. Trends in Molecular Medicine, 29(7):554–566.
6 Peng, W., Wu, R., Dai, W., and Yu, N. (2023). Identifying cancer driver genes based on multi-view heterogeneous graph convolutional network and self-attention mechanism. BMC Bioinformatics, 24(1):16.
7 Pratt, D., Chen, J., Pillich, R., Rynkov, V., Gary, A., Demchak, B., and Ideker, T. (2017). Ndex 2.0: a clearinghouse for research on cancer pathways. Cancer Research, 77(21):e58–e61.
8 Rogers, M. F., Gaunt, T. R., and Campbell, C. (2020). Prediction of driver variants in the cancer genome via machine learning methodologies. Briefings in Bioinformatics, 22(4). bbaa250.
9 Schulte-Sasse, R., Budach, S., Hnisz, D., and Marsico, A. (2021). Integration of multi- omics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms. Nature Machine Intelligence, 3(6):513–526.
10 Song, H., Yin, C., Li, Z., Feng, K., Cao, Y., Gu, Y., and Sun, H. (2023). Identification of cancer driver genes by integrating multiomics data with graph neural networks. Metabolites, 13(3):339.
11 Wang, L., Zhou, J., Wang, X., Wang, Y., and Li, J. (2024). MCDHGN: heterogeneous network-based cancer driver gene prediction and interpretability analysis. Bioinfor- matics, 40(6):btae362.
12 WHO, W. H. O. (2024). Global cancer burden growing, amidst mounting need for ser- vices. https://shorturl.at/8AUlY [Accessed: May 2025].
13 Zhang, H., Lin, C., Chen, Y., Shen, X., Wang, R., Chen, Y., and Lyu, J. (2025). Enhanc- ing molecular network-based cancer driver gene prediction using machine learning approaches: Current challenges and opportunities. Journal of Cellular and Molecular Medicine, 29(1):e70351.
14 Zhang, X.-M., Liang, L., Liu, L., and Tang, M.-J. (2021). Graph neural networks and their current applications in bioinformatics. Frontiers in Genetics, 12.