Abstract
Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a neurological genetic illness that inflicts severe symptoms after the onset occurs. Age of onset represents the moment a patient starts to experience the symptoms of a disease. An accurate prediction of this event can improve clinical and operational guidelines that define the work of doctors, nurses, and operational staff. In this work, we transform family trees into compact vectors, that is, embeddings, and handle these as input features to predict the age of onset of patients with TTR-FAP. Our purpose is to evaluate how information present in genealogical trees can be transformed and used to improve a regression-based setting for TTR-FAP age of onset prediction. Our results show that by combining manual and graph-embeddings features there is a decrease in the mean prediction error when there is less information regarding a patient’s family. With this work, we open the way for future work in representation learning for genealogical data, enabling a more effective exploitation of machine learning approaches.
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References
Andrade, C.: A peculiar form of peripheral neuropathy familiar atypical generalized amyloidosis with special involvement of the peripheral nerves. Brain: J. Neurol. 75(3), 408–27 (1952)
Chen, H., Perozzi, B., Al-Rfou, R., Skiena, S.: A Tutorial on Network Embeddings (2018). http://arxiv.org/abs/1808.02590
Gao, Z., et al.: Edge2vec: representation learning using edge semantics for biomedical knowledge discovery. BMC Bioinform. 20(1), 1–15 (2019). https://doi.org/10.1186/s12859-019-2914-2
Goyal, P., Ferrara, E.: Graph embedding techniques, applications, and performance: a survey. Knowl. Based Syst. 151, 78–94 (2018). https://doi.org/10.1016/j.knosys.2018.03.022, www.sciencedirect.com/science/article/pii/S0950705118301540
Grover, A., Leskovec, J.: node2vec. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2016 (2016). https://doi.org/10.1145/2939672.2939754
Lemos, C., et al.: Overcoming artefact: anticipation in 284 Portuguese kindreds with familial amyloid polyneuropathy (FAP) ATTRV30M. J. Neurol Neurosurg Psychiatry. 853, 326–330 (2014). https://doi.org/10.1136/jnnp-2013-305383, www.ncbi.nlm.nih.gov/pubmed/24046394
Lerique, S., Abitbol, J.L., Karsai, M.: Joint embedding of structure and features via graph convolutional networks. Appl. Netw. Sci. 5(1), 1–24 (2019). https://doi.org/10.1007/s41109-019-0237-x
Morris, C., Mutzel, P.: Towards a practical k-dimensional Weisfeiler-Leman algorithm (2019). http://arxiv.org/abs/1904.01543
Motoda, H., Liu, H.: Feature Selection, Extraction and Construction. Communication of IICM (Institute of Information and Computing Machinery, Taiwan) 5, 67–72 (2002). http://www.ar.sanken.osaka-u.ac.jp/motoda/papers/fdws02.pdf
Narayanan, A., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., Jaiswa, S.: graph2vec: learning distributed representations of graphs (2017). https://arxiv.org/pdf/1707.05005.pdf
Pedroto, M., Jorge, A., Mendes-Moreira, J., Coelho, T.: Predicting age of onset in TTR-FAP patients with genealogical features. In: Hollmén, J., McGregor, C., Soda, P., Kane, B. (eds.) 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018, Karlstad, Sweden, 18–21 June 2018, pp. 199–204. IEEE Computer Society (2018). https://doi.org/10.1109/CBMS.2018.00042
Pedroto, M., Jorge, A., Mendes-Moreira, J., Coelho, T.: Impact of genealogical features in transthyretin familial amyloid polyneuropathy age of onset prediction. In: Fdez-Riverola, F., Mohamad, M.S., Rocha, M., De Paz, J.F., González, P. (eds.) PACBB2018 2018. AISC, vol. 803, pp. 35–42. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-98702-6_5
Rozemberczki, B., Kiss, O., Sarkar, R.: Karate club: an API oriented open-source python framework for unsupervised learning on graphs. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 3125–3132. CIKM 2020, Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3340531.3412757
Sun, Y., Garcia-Pueyo, L., Wendt, J.B., Najork, M., A. Broder: Learning effective embeddings for machine generated emails with applications to email category prediction. In: IEEE International Conference on Big Data (Big Data), pp. 1846–1855 (2018). https://doi.org/10.1109/BigData.2018.8622048
Zhang, D., Yin, J., Zhu, X., Zhang, C.: Network representation learning: a survey (2017). http://arxiv.org/abs/1801.05852
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This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020 and by Centro Hospitalar do Porto (ChPorto).
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Pedroto, M., Jorge, A., Mendes-Moreira, J., Coelho, T. (2022). Improving the Prediction of Age of Onset of TTR-FAP Patients Using Graph-Embedding Features. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds) Progress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Science(), vol 13566. Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_16
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