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Tianyi Zhao, Yang Hu, Linda R Valsdottir, Tianyi Zang, and Jiajie Peng. 2021. Identifying drug-target interactions based on graph convolutional network and deep neural network. Briefings in bioinformatics, Vol. 22, 2 (2021), 2141--2150."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"crossref","unstructured":"Jiawei Zheng Qianli Ma Hao Gu and Zhenjing Zheng. 2021. Multi-view denoising graph auto-encoders on heterogeneous information networks for cold-start recommendation. 2338--2348. Jiawei Zheng Qianli Ma Hao Gu and Zhenjing Zheng. 2021. Multi-view denoising graph auto-encoders on heterogeneous information networks for cold-start recommendation. 2338--2348.","DOI":"10.1145\/3447548.3467427"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab473"},{"key":"e_1_3_2_1_58_1","unstructured":"Jingbo Zhou Shuangli Li Liang Huang Haoyi Xiong Fan Wang Tong Xu Hui Xiong and Dejing Dou. 2020. 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