A Comparative Study of CNN for Prediction of Human Cancer Types Integrating Protein-Protein Interaction Networks and Omics Data

• Marilio Freire de Almeida IFES
• Sérgio Nery Simões IFES
• Karin Satie Komati IFES

DOI: https://doi.org/10.5753/bsb.2024.245577

Resumo

This paper investigates convolutional neural networks (CNN) for predicting cancer types by integrating protein-protein interaction (PPI) networks with omics data. While [Chuang et al. 2021] employed a single 3-layer CNN, we explore ten different architectures, including a custom model developed by our team (CNN2Layers), following their methodology. By evaluating the strengths and weaknesses of these models, we aim to identify the most effective CNN for accurately predicting various human cancers. Our proposed model achieved state-of-the-art performance using fewer layers. Interestingly, the simpler architectures achieved superior results, indicating their effectiveness in handling the specific characteristics of the dataset.

Referências

Allemani, C., Matsuda, T., Di Carlo, V., Harewood, R., Matz, M., Nikšić, M., Bonaventure, A., Valkov, M., Johnson, C. J., Estève, J., et al. (2018). Global surveillance of trends in cancer survival 2000–14 (concord-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. The Lancet, 391(10125):1023–1075.

Aranda, B., Achuthan, P., Alam-Faruque, Y., Armean, I., Bridge, A., Derow, C., Feuermann, M., Ghanbarian, A., Kerrien, S., Khadake, J., et al. (2010). The intact molecular interaction database in 2010. Nucleic acids research, 38(suppl 1):D525–D531.

Barabási, A., Gulbahce, N., and Loscalzo, J. (2010). Network medicine: a network-based approach to human disease. Nature Reviews Genetics, 12:56–68.

Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R. L., Soerjomataram, I., and Jemal, A. (2024). Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 74(3):229–263.

Calderone, A., Iannuccelli, M., Peluso, D., and Licata, L. (2020). Using the mint database to search protein interactions. Current Protocols in Bioinformatics, 69(1):e93.

Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251–1258.

Chuang, Y.-H., Huang, S.-H., Hung, T.-M., Lin, X.-Y., Lee, J.-Y., Lai, W.-S., and Yang, J.-M. (2021). Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data. Scientific reports, 11(1):20691.

Dagogo-Jack, I. and Shaw, A. T. (2018). Tumour heterogeneity and resistance to cancer therapies. Nature reviews Clinical oncology, 15(2):81–94.

He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.

Horgon, R. and Kenny, L. (2011). Sac review: Omic technologies: genomics, transcriptomics. Proteomics and metabolomics. TOG, 13(189-195):25.

Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708.

Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., and Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4):541–551.

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.

Lombardo, E., Kurz, C., Marschner, S., Avanzo, M., Gagliardi, V., Fanetti, G., Franchin, G., Stancanello, J., Corradini, S., Niyazi, M., et al. (2021). Distant metastasis time to event analysis with cnns in independent head and neck cancer cohorts. Scientific reports, 11(1):6418.

Ma, S. and Zhang, Z. (2018). Omicsmapnet: Transforming omics data to take advantage of deep convolutional neural network for discovery. arXiv preprint arXiv:1804.05283.

Matsubara, T., Ochiai, T., Hayashida, M., Akutsu, T., and Nacher, J. C. (2019). Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles. Journal of Bioinformatics and Computational Biology, 17(03):1940007. PMID: 31288636.

Mewes, H.-W., Frishman, D., Mayer, K. F., Münsterkötter, M., Noubibou, O., Pagel, P., Rattei, T., Oesterheld, M., Ruepp, A., and Stümpflen, V. (2006). Mips: analysis and annotation of proteins from whole genomes in 2005. Nucleic acids research, 34(suppl 1):D169–D172.

Oughtred, R., Rust, J., Chang, C., Breitkreutz, B.-J., Stark, C., Willems, A., Boucher, L., Leung, G., Kolas, N., Zhang, F., et al. (2021). The biogrid database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Science, 30(1):187–200.

Qian, Y., Daza, J., Itzel, T., Betge, J., Zhan, T., Marmé, F., and Teufel, A. (2021). Prognostic cancer gene expression signatures: Current status and challenges. Cells, 10(3).

Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9.

Xenarios, I., Rice, D. W., Salwinski, L., Baron, M. K., Marcotte, E. M., and Eisenberg, D. (2000). Dip: the database of interacting proteins. Nucleic acids research, 28(1):289–291.

Zhou, L.-Q., Wu, X.-L., Huang, S.-Y., Wu, G.-G., Ye, H.-R., Wei, Q., Bao, L.-Y., Deng, Y.-B., Li, X.-R., Cui, X.-W., et al. (2020). Lymph node metastasis prediction from primary breast cancer us images using deep learning. Radiology, 294(1):19–28.