A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
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DOI: 10.1038/s41467-017-00680-8
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Cited by:
- Yuxuan Wang & Ying Xia & Junchi Yan & Ye Yuan & Hong-Bin Shen & Xiaoyong Pan, 2023. "ZeroBind: a protein-specific zero-shot predictor with subgraph matching for drug-target interactions," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
- Jiahua Rao & Jiancong Xie & Qianmu Yuan & Deqin Liu & Zhen Wang & Yutong Lu & Shuangjia Zheng & Yuedong Yang, 2024. "A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
- Qing Ye & Chang-Yu Hsieh & Ziyi Yang & Yu Kang & Jiming Chen & Dongsheng Cao & Shibo He & Tingjun Hou, 2021. "A unified drug–target interaction prediction framework based on knowledge graph and recommendation system," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
- Xiaomin Liang & Daifeng Li & Min Song & Andrew Madden & Ying Ding & Yi Bu, 2019. "Predicting biomedical relationships using the knowledge and graph embedding cascade model," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-23, June.
- Wen Zhang & Xiang Yue & Guifeng Tang & Wenjian Wu & Feng Huang & Xining Zhang, 2018. "SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions," PLOS Computational Biology, Public Library of Science, vol. 14(12), pages 1-21, December.
- Mingxuan Che & Kui Yao & Chao Che & Zhangwei Cao & Fanchen Kong, 2021. "Knowledge-Graph-Based Drug Repositioning against COVID-19 by Graph Convolutional Network with Attention Mechanism," Future Internet, MDPI, vol. 13(1), pages 1-10, January.
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