Dual-channel graph learning reveals similarity and complementarity in protein-protein interaction networks.
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| Title: | Dual-channel graph learning reveals similarity and complementarity in protein-protein interaction networks. |
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| Authors: | Tang T; School of Modern Posts, Nanjing University of Posts and Telecommunications, Jiangsu, China., Shen T; School of Modern Posts, Nanjing University of Posts and Telecommunications, Jiangsu, China., Li W; School of Modern Posts, Nanjing University of Posts and Telecommunications, Jiangsu, China., Chen Y; Department of Computer Science, University of Tsukuba, Ibaraki, Japan., Yuan S; School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China., Liu Y; College of Computer Science and Electronic Engineering, Hunan University, Hunan, China.; Yuelushan Laboratory, Changsha, China., Yang X; College of Computer Science and Electronic Engineering, Hunan University, Hunan, China.; Yuelushan Laboratory, Changsha, China., Luo X; College of Biology, Hunan University, Hunan, China. |
| Source: | PLoS computational biology [PLoS Comput Biol] 2026 May 20; Vol. 22 (5), pp. e1013941. Date of Electronic Publication: 2026 May 20 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1553-7358 |
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| DOI: | 10.1371/journal.pcbi.1013941 |
