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Li X, Li Q, Li D, Qian H, Wang J. Contrastive learning of graphs under label noise. Neural Netw 2024; 172:106113. [PMID: 38232430 DOI: 10.1016/j.neunet.2024.106113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 12/17/2023] [Accepted: 01/05/2024] [Indexed: 01/19/2024]
Abstract
In the domain of graph-structured data learning, semi-supervised node classification serves as a critical task, relying mainly on the information from unlabeled nodes and a minor fraction of labeled nodes for training. However, real-world graph-structured data often suffer from label noise, which significantly undermines the performance of Graph Neural Networks (GNNs). This problem becomes increasingly severe in situations where labels are scarce. To tackle this issue of sparse and noisy labels, we propose a novel approach Contrastive Robust Graph Neural Network (CR-GNN), Firstly, considering label sparsity and noise, we employ unsupervised contrastive loss and further incorporate homophily in the graph structure, thus introducing neighbor contrastive loss. Moreover, data augmentation is typically used to construct positive and negative samples in contrastive learning, which may result in inconsistent prediction outcomes. Based on this, we propose a dynamic cross-entropy loss, which selects the nodes with consistent predictions as reliable nodes for cross-entropy loss and benefits to mitigate the overfitting to labeling noise. Finally, we propose cross-space consistency to narrow the semantic gap between the contrast and classification spaces. Extensive experiments on multiple publicly available datasets demonstrate that CR-GNN notably outperforms existing methods in resisting label noise.
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Affiliation(s)
- Xianxian Li
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004, China; Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China; School of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004, China
| | - Qiyu Li
- School of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004, China
| | - De Li
- School of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004, China
| | - Haodong Qian
- School of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004, China
| | - Jinyan Wang
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004, China; Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China; School of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004, China.
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Sun H, Wang J, Wu H, Lin S, Chen J, Wei J, Lv S, Xiong Y, Wei DQ. A Multimodal Deep Learning Framework for Predicting PPI-Modulator Interactions. J Chem Inf Model 2023; 63:7363-7372. [PMID: 38037990 DOI: 10.1021/acs.jcim.3c01527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
Protein-protein interactions (PPIs) are essential for various biological processes and diseases. However, most existing computational methods for identifying PPI modulators require either target structure or reference modulators, which restricts their applicability to novel PPI targets. To address this challenge, we propose MultiPPIMI, a sequence-based deep learning framework that predicts the interaction between any given PPI target and modulator. MultiPPIMI integrates multimodal representations of PPI targets and modulators and uses a bilinear attention network to capture intermolecular interactions. Experimental results on our curated benchmark data set show that MultiPPIMI achieves an average AUROC of 0.837 in three cold-start scenarios and an AUROC of 0.994 in the random-split scenario. Furthermore, the case study shows that MultiPPIMI can assist molecular docking simulations in screening inhibitors of Keap1/Nrf2 PPI interactions. We believe that the proposed method provides a promising way to screen PPI-targeted modulators.
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Affiliation(s)
- Heqi Sun
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jianmin Wang
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon 21983, Republic of Korea
| | - Hongyan Wu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Shenggeng Lin
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Junwei Chen
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jinghua Wei
- Department of Chemistry, University of Toronto, Toronto M5R 0A3, Canada
| | - Shuai Lv
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Peng Cheng National Laboratory, Shenzhen 518055, China
- Zhongjing Research and Industrialization Institute of Chinese Medicine, Nanyang 473006, China
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