Abstract
Network embedding has become an important representation technique recently as an effective method to solve the heterogeneity of data relations of non-Euclidean learning. With the aims of learning low-dimensional latent representations of nodes in a network, the learned representations can be used as efficient features for various network-based tasks, such as classification, clustering, link prediction and visualization. In recent years, various low-dimensional graph embedding methods have been proposed. Yet few of them are analyzed and studied in a systematic experiment in the prediction of lncRNA-protein interaction (LPI), especially for the newly available methods. Here, we divide these methods into three categories, i.e., factorization-based, random walk-based and deep learning-based method, and select six representative methods in them for predicting LPIs. Finally, these state-of-the-art network embedding methods are evaluated on five benchmark datasets containing three ones in human and two in plants. Experimental results demonstrate that recent network embedding methods, e.g., metapath2vec, achieve better prediction performances. The data and code in this study are available at: https://github.com/zpliulab/Bconstract_embedding.
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References
Leskovec, J., Sosič, R.: SNAP: A general-purpose network analysis and graph-mining library. ACM Trans. Intell. Syst. Technol. 8, 1–20 (2016)
Xiao, Y., Zhang, J., Deng, L.: Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks. Sci. Rep. 7, 3664 (2017)
Zhang, W., et al.: Predicting drug-disease associations and their therapeutic function based on the drug-disease association bipartite network. Methods 145, 51–59 (2018)
Li, T., et al.: A scored human protein–protein interaction network to catalyze genomic interpretation. Nat. Methods 14, 61–64 (2017)
Cai, H., Zheng, V.W., Chang, K.C.-C.: A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Trans. Knowl. Data Eng. 30, 1616–1637 (2018)
Goyal, P., Ferrara, E.: Graph embedding techniques, applications, and performance: A survey. Knowl.-Based Syst. 151, 78–94 (2018)
Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: A survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29, 2724–2743 (2017)
Qiu, J., et al.: Network embedding as matrix factorization: Unifying DeepWalk, LINE, PTE, and node2vec. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 459–467. ACM (2018). https://doi.org/10.1145/3159652.3159706
Chen, H., Perozzi, B., Hu, Y., Skiena, S.: HARP: Hierarchical representation learning for networks. 8. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32(1). https://ojs.aaai.org/index.php/AAAI/article/view/11849
Velickovic, P., et al.: Deep graph infomax. In: Proceedings of the Seventh International Conference on Learning Representations, vol. 46 (2019)
Veličković, P., et al.: Graph Attention Networks. ArXiv171010903 Cs Stat (2018)
Xue, G., et al.: Dynamic network embedding survey. Neurocomputing 472, 212–223 (2022)
Wang, Y., et al.: De novo prediction of RNA–protein interactions from sequence information. Mol. BioSyst. 9, 133–142 (2013)
Su, C., Tong, J., Zhu, Y., Cui, P., Wang, F.: Network embedding in biomedical data science. Brief. Bioinform. 21, 182–197 (2020)
Nelson, W., et al.: To embed or not: Network embedding as a paradigm in computational biology. Front. Genet. 10, 381 (2019)
Dong, Y., Chawla, N.V., Swami, A.: 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. ACM (2017). https://doi.org/10.1145/3097983.3098036
Peng, L., Tan, J., Tian, X., Zhou, L.: EnANNDeep: An ensemble-based lncRNA–protein interaction prediction framework with adaptive k-nearest neighbor classifier and deep models. Interdiscip. Sci. Comput. Life Sci. 14, 209–232 (2022)
Ahmed, A., Shervashidze, N., Narayanamurthy, S., Josifovski, V., Smola, A.J.: Distributed large-scale natural graph factorization. In: Proceedings of the 22nd International Conference on World Wide Web - WWW 2013, pp. 37–48. ACM Press (2013). https://doi.org/10.1145/2488388.2488393
Cao, S., Lu, W., Xu, Q.: GraRep: Learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 891–900. ACM (2015). https://doi.org/10.1145/2806416.2806512
Ou, M., Cui, P., Pei, J., Zhang, Z., Zhu, W.: Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1105–1114. ACM (2016). https://doi.org/10.1145/2939672.2939751
Chen, T., et al.: SVDFeature: A toolkit for feature-based collaborative filtering. J. Mach. Learn. Res. 13, 3619–3622 (2012)
Dai, W., et al.: Matrix factorization-based prediction of novel drug indications by integrating genomic space. Comput. Math. Methods Med. 2015, 1–9 (2015)
Lei, X., Yang, X., Fujita, H.: Random walk based method to identify essential proteins by integrating network topology and biological characteristics. Knowl.-Based Syst. 167, 53–67 (2019)
Xie, G., Huang, B., Sun, Y., Wu, C., Han, Y.: RWSF-BLP: A novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation. Mol. Genet. Genomics 296(3), 473–483 (2021). https://doi.org/10.1007/s00438-021-01764-3
Peng, J., Guan, J., Shang, X.: Predicting Parkinson’s disease genes based on Node2vec and autoencoder. Front. Genet. 10, 226 (2019)
Gu, S., Milenkovic, T.: Graphlets versus node2vec and struc2vec in the task of network alignment. ArXiv180504222 Phys (2018)
Zhang, Y., Tang, M.: Consistency of random-walk based network embedding algorithms. ArXiv210107354 Cs Stat (2021)
Chami, I., Abu-El-Haija, S., Perozzi, B., Ré, C., Murphy, K.: Machine Learning on Graphs: A Model and Comprehensive Taxonomy. ArXiv200503675 Cs Stat (2021)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014). https://doi.org/10.1145/2623330.2623732
Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM (2016). https://doi.org/10.1145/2939672.2939754
Shinde, P.P., Shah, S.: A review of machine learning and deep learning applications. In: Proceedings of the 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1–6. IEEE (2018). https://doi.org/10.1109/ICCUBEA.2018.8697857
Tang, J., et al.: LINE: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077. International World Wide Web Conferences Steering Committee (2015). https://doi.org/10.1145/2736277.2741093
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225–1234. ACM (2016). https://doi.org/10.1145/2939672.2939753
Acknowledgements
This work was partially supported by National Natural Science Foundation of China (No. 61973190); National Key Research and Development Program of China (No. 2020YFA0712402); Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project 2019JZZY010423); Natural Science Foundation of Shandong Province of China (ZR2020ZD25); the Innovation Method Fund of China (Ministry of Science and Technology of China, 2018IM020200); the Tang Scholar and the program of Qilu Young Scholar of Shandong University.
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Zhao, G., Li, P., Liu, ZP. (2022). A Comparison Study of Predicting lncRNA-Protein Interactions via Representative Network Embedding Methods. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_1
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