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Golzadeh, Ali, Kamandi, Ali, Rahami, Hossein. (1402). An attributed network embedding method to predict missing links in protein-protein interaction networks. , 55(1), 79-99. doi: 10.22059/jac.2023.92758
Ali Golzadeh; Ali Kamandi; Hossein Rahami. "An attributed network embedding method to predict missing links in protein-protein interaction networks". , 55, 1, 1402, 79-99. doi: 10.22059/jac.2023.92758
Golzadeh, Ali, Kamandi, Ali, Rahami, Hossein. (1402). 'An attributed network embedding method to predict missing links in protein-protein interaction networks', , 55(1), pp. 79-99. doi: 10.22059/jac.2023.92758
Golzadeh, Ali, Kamandi, Ali, Rahami, Hossein. An attributed network embedding method to predict missing links in protein-protein interaction networks. , 1402; 55(1): 79-99. doi: 10.22059/jac.2023.92758

An attributed network embedding method to predict missing links in protein-protein interaction networks

Journal of Algorithms and Computation
دوره 55، شماره 1، شهریور 2023، صفحه 79-99    اصل مقاله (427.64 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jac.2023.92758
نویسندگان
Ali Golzadeh1؛ Ali Kamandi* 2؛ Hossein Rahami1
1School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran
2Department of Algorithms and Computation, School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran
چکیده
Predicting missing links in noisy protein-protein interaction networks is an essential~computational method. Recently, attributed network embedding methods have been shown to be significantly effective in generating low-dimensional representations of nodes to predict links; in these representations, both the nodes'features and the network's topological information are preserved. Recent research suggests that models based on paths of length 3 between two nodes are more accurate than models based on paths of length 2 for predicting missing links in a protein-protein interaction network. In the present study, an attributed network embedding method termed ANE-SITI is recommended to combine protein sequence information and network topological information. In addition, to improve accuracy, network topological information also considers paths of length 3 between two proteins. The results of this experiment demonstrate that ANE-SITI outperforms the compared methods on various~protein-protein interaction (PPI) networks.
 
کلیدواژه‌ها
link prediction؛ protein-protein interaction networks؛ attributed network embedding؛ biased random walks
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تعداد مشاهده مقاله: 211
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