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Complex Attributed Network Embedding for medical complication prediction

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Abstract

To assure the development of effective treatment plans, it is crucial for understanding the complication relationships among diseases. In practice, traditional statistical methods are widely used to find the complications of diseases despite the potential errors introduced by the discrepancies in medical records. Recently, with the advances of network embedding techniques, it is promising to predict medical complications in properly constructed biomedical networks. However, due to the variety and sparsity of disease attributes, it is challenging to measure the similarity between attributes of different disease nodes, which seriously interferes the medical complication prediction task. To deal with this problem, in this paper, we propose a novel data-driven Complex Attributed Network Embedding (CANE) method to learn representation for each disease, which can better solve the variety and sparsity. Specifically, we first estimate the initial low-level representations of disease attributes via a matrix factorization technique and then refine the representations via several well-designed attribute modeling modules. Along this line, we introduce aggregation functions to preserve local structure information in the representations of diseases and apply them for complication prediction task. Finally, comprehensive experiments on real-world biomedical data clearly validate the effectiveness of CANE.

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  2. https://github.com/tensorflow/tensorflow.

References

  1. Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473

  2. Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: Advances in neural information processing systems, pp 2787–2795

  3. Cai L, Wang WY (2017) Kbgan: adversarial learning for knowledge graph embeddings. arXiv preprint arXiv:1711.04071

  4. Camilleri M, Malhi H, Acosta A (2017) Gastrointestinal complications of obesity. Gastroenterology 152(7):1656–1670

    Article  Google Scholar 

  5. Chang S, Han W, Tang J, Qi GJ, Aggarwal CC, Huang TS (2015) Heterogeneous network embedding via deep architectures. In: Proceedings of the 21th ACM SIGKDD International conference on knowledge discovery and data mining. ACM, pp 119–128

  6. Cho H, Berger B, Peng J (2016) Compact integration of multi-network topology for functional analysis of genes. Cell Syst 3(6):540–548

    Article  Google Scholar 

  7. Christopoulou F, Miwa M, Ananiadou S (2019) Connecting the dots: document-level neural relation extraction with edge-oriented graphs. arXiv preprint arXiv:1909.00228

  8. DeAngelis LM (2016) Neurologic complications of cancer. Holland-Frei Cancer Medicine 1–15

  9. Dettmers T, Minervini P, Stenetorp P, Riedel S (2017) Convolutional 2d knowledge graph embeddings

  10. Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

  11. Dong Y, Chawla NV, Swami A (2017) Metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 135–144

  12. Du Y, Luo P, Hong X, Xu T, Zhang Z, Ren C, Zheng Y, Chen E (2021) Inheritance-guided hierarchical assignment for clinical automatic diagnosis. In: International conference on database systems for advanced applications. Springer, pp 461–477

  13. Ezzat A, Wu M, Li XL, Kwoh CK (2017) Drug-target interaction prediction using ensemble learning and dimensionality reduction. Methods 129:81–88

    Article  Google Scholar 

  14. Fang L, Zhang L, Wu H, Xu T, Zhou D, Chen E (2021) Patent2vec: Multi-view representation learning on patent-graphs for patent classification. World Wide Web 24(5):1791–1812

    Article  Google Scholar 

  15. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680

  16. Grover A, Leskovec J (2016) Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 855–864

  17. Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in neural information processing systems, pp 1024–1034

  18. Hu JX, Thomas CE, Brunak S (2016) Network biology concepts in complex disease comorbidities. Nature Rev Genet 17(10):615

    Article  Google Scholar 

  19. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167

  20. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907

  21. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  22. Kulmanov M, Khan MA, Hoehndorf R (2018) Deepgo: predicting protein functions from sequence and interactions using a deep ontology-aware classifier. Bioinformatics 34(4):660–668

    Article  Google Scholar 

  23. Li S, Zhou J, Xu T, Dou D, Xiong H (2021) Geomgcl: geometric graph contrastive learning for molecular property prediction. arXiv preprint arXiv:2109.11730

  24. Li S, Zhou J, Xu T, Huang L, Wang F, Xiong H, Huang W, Dou D, Xiong H (2021)Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp 975–985

  25. Li S, Zhou J, Xu T, Liu H, Lu X, Xiong H (2020) Competitive analysis for points of interest. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1265–1274

  26. Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: Twenty-ninth AAAI conference on artificial intelligence

  27. Liu J, Chen S, Wang B, Zhang J, Li N, Xu T (2021) Attention as relation: learning supervised multi-head self-attention for relation extraction. In: Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence, pp 3787–3793

  28. Ma T, Xiao C, Zhou J, Wang F (2018) Drug similarity integration through attentive multi-view graph auto-encoders. arXiv preprint arXiv:1804.10850

  29. Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barabási AL (2015) Uncovering disease-disease relationships through the incomplete interactome. Science 347(6224):1257601

    Article  Google Scholar 

  30. Mikolov T, Chen K, Corrado G, rey Dean J (2013) E cient estimation of word representations in vector space. corr abs/1301.3781. hp. arXiv. org/abs/1301.3781 (2013)

  31. Mnih A, Salakhutdinov RR (2008) Probabilistic matrix factorization. In: Advances in neural information processing systems, pp 1257–1264

  32. Nelson W, Zitnik M, Wang B, Leskovec J, Goldenberg A, Sharan R (2019) To embed or not: network embedding as a paradigm in computational biology. Frontiers in genetics 10

  33. Nickel M, Tresp V, Kriegel HP (2011) A three-way model for collective learning on multi-relational data. In: International conference on international conference on machine learning

  34. Sarwar BM, Karypis G, Konstan JA, Riedl J et al (2001) Item-based collaborative filtering recommendation algorithms. Www 1:285–295

  35. Shang C, Tang Y, Huang J, Bi J, He X, Zhou B (2019) End-to-end structure-aware convolutional networks for knowledge base completion. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 3060–3067

  36. Shi C, Han X, Song L, Wang X, Wang S, Du J, Philip SY (2019) Deep collaborative filtering with multi-aspect information in heterogeneous networks. IEEE Trans knowledge Data Eng 33(4):1413–1425

    Article  Google Scholar 

  37. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  38. Su C, Tong J, Zhu Y, Cui P, Wang F (2020) Network embedding in biomedical data science. Br Bioinform 21(1):182–197

    Article  Google Scholar 

  39. Sun Y, Wang S, Li Y, Feng S, Chen X, Zhang H, Tian X, Zhu D, Tian H, Wu H (2019) Ernie: enhanced representation through knowledge integration. arXiv preprint arXiv:1904.09223

  40. Trouillon T, Welbl J, Riedel S, Gaussier É, Bouchard G (2016) Complex embeddings for simple link prediction. In: International conference on machine learning, pp 2071–2080

  41. Wang H, Chen E, Liu Q, Xu T, Du D, Su W, Zhang X (2018) A united approach to learning sparse attributed network embedding. In: 2018 IEEE international conference on data mining (ICDM). IEEE, pp 557–566

  42. Wang X, Bo D, Shi C, Fan S, Ye Y, Yu PS (2020) A survey on heterogeneous graph embedding: methods, techniques, applications and sources. arXiv preprint arXiv:2011.14867

  43. Wang YB, You ZH, Li X, Jiang TH, Chen X, Zhou X, Wang L (2017) Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network. Mol BioSyst 13(7):1336–1344

    Article  Google Scholar 

  44. Xu K, Yang Z, Kang P, Wang Q, Liu W (2019) Document-level attention-based bilstm-crf incorporating disease dictionary for disease named entity recognition. Comput biol Med 108:122–132

    Article  Google Scholar 

  45. Xu T, Zhu H, Zhong H, Liu G, Xiong H, Chen E (2018) Exploiting the dynamic mutual influence for predicting social event participation. IEEE Trans Knowl Data Eng 31(6):1122–1135

    Article  Google Scholar 

  46. Yang B, Yih Wt, He X, Gao J, Deng L (2014) Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575

  47. Yang C, Liu Z, Zhao D, Sun M, Chang E (2015) Network representation learning with rich text information. In: Twenty-fourth international joint conference on artificial intelligence

  48. Yoon W, So CH, Lee J, Kang J (2019) Collabonet: collaboration of deep neural networks for biomedical named entity recognition. BMC Bioinform 20(10):249

    Article  Google Scholar 

  49. Yue X, Wang Z, Huang J, Parthasarathy S, Moosavinasab S, Huang Y, Lin SM, Zhang W, Zhang P, Sun H (2020) Graph embedding on biomedical networks: methods, applications and evaluations. Bioinform. 36(4):1241–1251

    Google Scholar 

  50. Zhang C, Fan W, Du N, Yu PS (2016) Mining user intentions from medical queries: a neural network based heterogeneous jointly modeling approach. In: Proceedings of the 25th international conference on world wide web. International world wide web conferences steering committee, pp 1373–1384

  51. Zhang D, Yin J, Zhu X, Zhang C (2016) Homophily, structure, and content augmented network representation learning. In: 2016 IEEE 16th International conference on data mining (ICDM). IEEE, pp 609–618

  52. Zhang L, Zhou D, Zhu H, Xu T, Zha R, Chen E, Xiong H (2021) Attentive heterogeneous graph embedding for job mobility prediction. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp 2192–2201

  53. Zhang W, Chen Y, Li D, Yue X (2018) Manifold regularized matrix factorization for drug-drug interaction prediction. J Biomed Inform 88:90–97

    Article  Google Scholar 

  54. Zhang W, Yue X, Lin W, Wu W, Liu R, Huang F, Liu F (2018) Predicting drug-disease associations by using similarity constrained matrix factorization. BMC Bioinform 19(1):1–12

    Article  Google Scholar 

  55. Zhang Y, Wallace B (2015) A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:1510.03820

  56. Zheng Z, Wang C, Xu T, Shen D, Qin P, Huai B, Liu T, Chen E (2021) Drug package recommendation via interaction-aware graph induction. In: Proceedings of the web conference 2021, pp 1284–1295

  57. Zheng Z, Xu T, Qin C, Liao X, Zheng Y, Liu T, Tong G (2020) Multi-source contextual collaborative recommendation for medicine. J Comput Res Develop 57(8):1741

    Google Scholar 

  58. Žitnik M, Janjić V, Larminie C, Zupan B, Pržulj N (2013) Discovering disease-disease associations by fusing systems-level molecular data. Sci Rep 3(1):1–9

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone (HZQB-KCZYB-2020083), the National Natural Science Foundation of China (Grant No. 62072423), and Foshan HKUST Projects (FSUST21-FYTRI01A, FSUST21-FYTRI02A).

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Correspondence to Hui Xiong or Tong Xu.

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Zhang, Z., Xiong, H., Xu, T. et al. Complex Attributed Network Embedding for medical complication prediction. Knowl Inf Syst 64, 2435–2456 (2022). https://doi.org/10.1007/s10115-022-01712-6

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