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Journal Abstract Search
1254 related items for PubMed ID: 32693790
1. Graph-based prediction of Protein-protein interactions with attributed signed graph embedding. Yang F, Fan K, Song D, Lin H. BMC Bioinformatics; 2020 Jul 21; 21(1):323. PubMed ID: 32693790 [Abstract] [Full Text] [Related]
3. Sequence-based prediction of protein protein interaction using a deep-learning algorithm. Sun T, Zhou B, Lai L, Pei J. BMC Bioinformatics; 2017 May 25; 18(1):277. PubMed ID: 28545462 [Abstract] [Full Text] [Related]
5. Multimodal deep representation learning for protein interaction identification and protein family classification. Zhang D, Kabuka M. BMC Bioinformatics; 2019 Dec 02; 20(Suppl 16):531. PubMed ID: 31787089 [Abstract] [Full Text] [Related]
6. SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction. Li X, Han P, Wang G, Chen W, Wang S, Song T. BMC Genomics; 2022 Jun 27; 23(1):474. PubMed ID: 35761175 [Abstract] [Full Text] [Related]
7. Deep Network Embedding for Graph Representation Learning in Signed Networks. Shen X, Chung FL. IEEE Trans Cybern; 2020 Apr 27; 50(4):1556-1568. PubMed ID: 30307885 [Abstract] [Full Text] [Related]
9. AE-LGBM: Sequence-based novel approach to detect interacting protein pairs via ensemble of autoencoder and LightGBM. Sharma A, Singh B. Comput Biol Med; 2020 Oct 27; 125():103964. PubMed ID: 32911276 [Abstract] [Full Text] [Related]
14. A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks. Hasibi R, Michoel T. BMC Bioinformatics; 2021 Oct 27; 22(1):525. PubMed ID: 34706640 [Abstract] [Full Text] [Related]
16. RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences. An JY, You ZH, Meng FR, Xu SJ, Wang Y. Int J Mol Sci; 2016 May 18; 17(5):. PubMed ID: 27213337 [Abstract] [Full Text] [Related]