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graph convolutional autoencoder and Generative Adversarial Network-Based Method for Predicting Drug-Target Interactions

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第1期19卷 455-464页

作者： Sun, Chang Xuan, Ping Zhang, Tiangang Ye, Yilin Heilongjiang Univ Dept Comp Sci & Technol Harbin 150080 Heilongjiang Peoples R China Heilongjiang Univ Dept Math Sci Harbin 150080 Heilongjiang Peoples R China

The computational prediction of novel drug-target interactions (DTIs) may effectively speed up the process of drug repositioning and reduce its costs. Most previous methods integrated multiple kinds of connections about drugs and targets by constructing shallow prediction models. These methods failed to deeply learn the low-dimension feature vectors for drugs and targets and ignored the distribution of these feature vectors. We proposed a graph convolutional autoencoder and generative adversarial network (GAN)-based method, GANDTI, to predict DTIs. We constructed a drug-target heterogeneous network to integrate various connections related to drugs and targets, i.e., the similarities and interactions between drugs or between targets and the interactions between drugs and targets. A graph convolutional autoencoder was established to learn the network embeddings of the drug and target nodes in a low-dimensional feature space, and the autoencoder deeply integrated different kinds of connections within the network. A GAN was introduced to regularize the feature vectors of nodes into a Gaussian distribution. Severe class imbalance exists between known and unknown DTIs. Thus, we constructed a classifier based on an ensemble learning model, LightGBM, to estimate the interaction propensities of drugs and targets. This classifier completely exploited all unknown DTIs and counteracted the negative effect of class imbalance. The experimental results indicated that GANDTI outperforms several state-of-the-art methods for DTI prediction. Additionally, case studies of five drugs demonstrated the ability of GANDTI to discover the potential targets for drugs.

关键词： Adversarial regularization drug-target interaction generative adversarial network graph convolutional autoencoder LightGBM

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graph convolutional autoencoder and Fully-Connected autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第5期25卷 1793-1804页

作者： Xuan, Ping Gao, Ling Sheng, Nan Zhang, Tiangang Nakaguchi, Toshiya Heilongjiang Univ Sch Comp Sci & Technol Harbin 150080 Peoples R China Heilongjiang Univ Sch Math Sci Harbin 150080 Peoples R China Chiba Univ Ctr Frontier Med Engn Chiba 2638522 Japan

Predicting novel uses for approved drugs helps in reducing the costs of drug development and facilitates the development process. Most of previous methods focused on the multi-source data related to drugs and diseases to predict the candidate associations between drugs and diseases. There are multiple kinds of similarities between drugs, and these similarities reflect how similar two drugs are from the different views, whereas most of the previous methods failed to deeply integrate these similarities. In addition, the topology structures of the multiple drug-disease heterogeneous networks constructed by using the different kinds of drug similarities are not fully exploited. We therefore propose GFPred, a method based on a graph convolutional autoencoder and a fully-connected autoencoder with an attention mechanism, to predict drug-related diseases. GFPred integrates drug-disease associations, disease similarities, three kinds of drug similarities and attributes of the drug nodes. Three drug-disease heterogeneous networks are constructed based on the different kinds of drug similarities. We construct a graph convolutional autoencoder module, and integrate the attributes of the drug and disease nodes in each network to learn the topology representations of each drug node and disease node. As the different kinds of drug attributes contribute differently to the prediction of drug-disease associations, we construct an attribute-level attention mechanism. A fully-connected autoencoder module is established to learn the attribute representations of the drug and disease nodes. Finally, the original features of the drug-disease node pairs are also important auxiliary information for their association prediction. A combined strategy based on a convolutional neural network is proposed to fully integrate the topology representations, the attribute representations, and the original features of the drug-disease pairs. The ablation studies showed the contributions of data related

关键词： Drugs Diseases Proteins Heterogeneous networks Chemicals Topology Network topology Prediction of drug-related diseases graph convolutional autoencoder fully-connected autoencoder attribute-level attention multiple kinds of drug similarities

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Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

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BMC BIOINFORMATICS 2023年第1期24卷 1-21页

作者： Chen, Peng Zheng, Haoran Univ Sci & Technol China Sch Comp Sci & Technol Jinzhai Rd 96 Hefei 230027 Peoples R China Univ Sci & Technol China Anhui Key Lab Software Engn Comp & Commun Jinzhai Rd 96 Hefei 230027 Peoples R China

BackgroundDrug-target interaction (DTI) prediction plays an important role in drug discovery and repositioning. However, most of the computational methods used for identifying relevant DTIs do not consider the invariance of the nearest neighbour relationships between drugs or targets. In other words, they do not take into account the invariance of the topological relationships between nodes during representation learning. It may limit the performance of the DTI prediction ***, we propose a novel graph convolutional autoencoder-based model, named SDGAE, to predict DTIs. As the graph convolutional network cannot handle isolated nodes in a network, a pre-processing step was applied to reduce the number of isolated nodes in the heterogeneous network and facilitate effective exploitation of the graph convolutional network. By maintaining the graph structure during representation learning, the nearest neighbour relationships between nodes in the embedding space remained as close as possible to the original ***, we demonstrated that SDGAE can automatically learn more informative and robust feature vectors of drugs and targets, thus exhibiting significantly improved predictive accuracy for DTIs.

关键词： Drug-target interaction Spatial consistency constraint graph convolutional autoencoder Deep learning

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MSGCA: Drug-Disease Associations Prediction Based on Multi-Similarities graph convolutional autoencoder

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第7期27卷 3686-3694页

作者： Wang, Ying Gao, Ying-Lian Wang, Juan Li, Feng Liu, Jin-Xing Qufu Normal Univ Sch Comp Sci Rizhao 276826 Peoples R China Qufu Normal Univ Qufu Normal Univ Lib Rizhao 276826 Peoples R China

Identifying drug-disease associations (DDAs) is critical to the development of drugs. Traditional methods to determine DDAs are expensive and inefficient. Therefore, it is imperative to develop more accurate and effective methods for DDAs prediction. Most current DDAs prediction methods utilize original DDAs matrix directly. However, the original DDAs matrix is sparse, which greatly affects the prediction consequences. Hence, a prediction method based on multi-similarities graph convolutional autoencoder (MSGCA) is proposed for DDAs prediction. First, MSGCA integrates multiple drug similarities and disease similarities using centered kernel alignment-based multiple kernel learning (CKA-MKL) algorithm to form new drug similarity and disease similarity, respectively. Second, the new drug and disease similarities are improved by linear neighborhood, and the DDAs matrix is reconstructed by weighted K nearest neighbor profiles. Next, the reconstructed DDAs and the improved drug and disease similarities are integrated into a heterogeneous network. Finally, the graph convolutional autoencoder with attention mechanism is utilized to predict DDAs. Compared with extant methods, MSGCA shows superior results on three datasets. Furthermore, case studies further demonstrate the reliability of MSGCA.

关键词： Drug-disease associations graph convolutional autoencoder linear neighborhood multi-similarities

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SGEGCAE: A Sparse Gating Enhanced graph convolutional autoencoder for Multi-omics Data Integration and Classification 20th

SGEGCAE: A Sparse Gating Enhanced Graph Convolutional Autoen...

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20th International Conference on Intelligent Computing (ICIC)

作者： Shang, Junliang Zhang, Limin Zhao, Linqian He, Xin Zhao, Yan Ge, Daohui Liu, Jin-Xing Li, Feng Qufu Normal Univ Sch Comp Sci Rizhao 276800 Peoples R China Univ Hlth & Rehabil Sci Sch Hlth & Life Sci Qingdao 266000 Peoples R China

ISBN: (纸本)9789819756889;9789819756896

Integration of multi-omics data is essential for obtaining comprehensive insights into molecular mechanisms of complex diseases. While several methods have been proposed for analyzing multi-omics data in various applications, challenges persist in effectively handling heterogeneous and rich multi-omics data. In this paper, a Sparse Gating Enhanced graph convolutional autoencoder, named SGEGCAE, is proposed for multi-omics data integration and classification. Specifically, an enhanced graph convolutional autoencoder is developed, which integrates a basic autoencoder with a sparse gating strategy, aiming to combine attribute information with topological structure information of the graph for obtaining more comprehensive feature representations. To address the inherent variability and fluctuations in different omics data quality among samples, true class probability is introduced into the SGEGCAE to acquire reliable classification confidence. Furthermore, a tensor fusion network is designed to explore both inter-omics and intra-omics relationships in the label space to achieve ultimately multi-omics integration and classification. Extensive biomedical classification experiments are carried out on four datasets. In these experiments, the superior performance of the SGEGCAE is clearly validated compared to some state-of-the-art integrative analysis methods, demonstrating that the SGEGCAE might serve as an alternative method for multi-omics data integration and classification. The code and datasets for the SGEGCAE are available online at https://***/CDMBlab/SGEGCAE.

关键词： Multi-omics Integration graph convolutional autoencoder Sparse Gating Strategy True Class Probability Tensor Fusion Network

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ScMOGAE: A graph convolutional autoencoder-Based Multi-omics Data Integration Framework for Single-Cell Clustering 20th

ScMOGAE: A Graph Convolutional Autoencoder-Based Multi-omics...

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20th International Symposium on Bioinformatics Research and Applications (ISBRA)

作者： Zhou, Benjie Jiang, Hongyang Wang, Yuezhu Gu, Yujie Sun, Huiyan Jilin Univ Sch Artificial Intelligence Changchun 130012 Peoples R China Jilin Univ Int Ctr Future Sci Changchun Peoples R China MOE Engn Res Ctr Knowledge Driven Human Machine Intel Changchun Peoples R China

ISBN: (纸本)9789819751273;9789819751280

The integration of single-cell multi-omics data is a significant step forward in our understanding of the complex biological systems at the cellular level. This approach allows for the simultaneous analysis of various molecular layers, and provides insights into the heterogeneity and clustering of cells. However, the fusion of single-cell multi-omics data poses several challenges on how to effectively represent joint distributions due to their high dimensionality, sparsity and dropout events. In this study, we propose a deep graph neural network framework for single-cell multi-omics data fusion(scMOGAE), which integrates scRNA-seq data and scATAC-seq data. Specifically, scMOGAE first estimates cell-cell similarity for each modality and then employs graph convolutional autoencoders to learn their latent embedded representations, respectively. Subsequently, scMOGAE aligns and weights adaptively to obtain joint representations of these two modalities for cell clustering. Furthermore, by incorporating cross-modality prediction in the training process, scMOGAE is capable of imputing missing data. Extensive experiments on multiple datasets demonstrate that scMOGAE achieves superior performance for single-cell clustering.

关键词： Multi-omics integration Single-cell clustering graph convolutional autoencoder Generative adversarial network

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Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder

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INFORMATION SCIENCES 2022年 608卷 1464-1479页

作者： He, Chaobo Zheng, Yulong Cheng, Junwei Tang, Yong Chen, Guohua Liu, Hai South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China Pazhou Lab Guangzhou 510335 Peoples R China

Community detection in attributed graph is of great application value and many related methods have been continually presented. However, existing methods for community detection in attributed graph still cannot well solve three key problems simultaneously: link information and attribute information fusion, prior information integration and over-lapping community detection. Aiming at these problems, in this paper we devise a semi -supervised overlapping community detection method named SSGCAE which is based on graph neural networks. This method is composed of three modules: graph convolutional autoencoder (GCAE), semi-supervision and modularity maximization, which are respec-tively utilized to fuse link information and attribute information, integrate prior informa-tion and detect overlapping communities. We treat GCAE as the backbone framework and train it by using the unified loss from these three modules. Through this way, these three modules are jointly correlated via the community membership representation, which is very beneficial to improve the overall performance. SSGCAE is comprehensively evaluated on synthetic and real attributed graphs, and experiment results show that it is very effec-tive and outperforms state-of-the-art baseline approaches.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Overlapping community detection Semi -supervised learning graph convolutional autoencoder graph neural networks Attributed graph

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Federated learning enabled graph convolutional autoencoder and factorization machine for potential friendship prediction in social networks

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INFORMATION FUSION 2024年 102卷

作者： Hu, He-xuan Cao, Chengcheng Hu, Qiang Zhang, Ye Hohai Univ Key Lab Water Big Data Technol Minist Water Resources Nanjing 211100 Peoples R China Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China

Friendships are the keystone of social networks. Predicting potential friendships of users in social networks has become a critical task in the real world. However, the computational models proposed by previous researchers do not effectively capture the behavior preferences of users, which limits the recommendation results. Moreover, the process of generating predicted values has not considered the cross-information among different features. Therefore, we design a potential friendship prediction model based on graph convolutional autoencoder and factorization machine (GCAFM). The GCAFM model uses a graph convolutional autoencoder to learn the hidden feature of users, which can make the similar users as close as possible in the embedding space. The hidden feature of the target users is then subjected to an element-to-element multiplication operation, and the product results are subsequently inputting into a factorization machine model that capable of feature crossover to get the predicted friendship. In addition, we use the federated learning framework to train the GCAFM model, which ensures the privacy of social network data. The experimental results on Douban and FilmTrust social network datasets show that the GCAFM model exceeds four comparison models under various kinds of evaluation metrics. The GCAFM model scored 0.856 and 0.850 under the AUC metric, 0.683 and 0.702 under the AUPR metric, 0.805 and 0.844 under the Precision metric on Douban and FilmTrust datasets.

关键词： Potential friendship prediction graph convolutional autoencoder Federated learning Data mining

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Deep anomaly detection in horizontal axis wind turbines using graphconvolutional autoencoders for Multivariate Time series

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Energy and AI 2022年第2期8卷 79-91页

作者： Eric Stefan Miele Fabrizio Bonacina Alessandro Corsini Department of Mechanical and Aerospace Engineering Sapienza University of RomeVia Eudossiana 118RomeI00184Italy

Wind power is one of the fastest-growing renewable energy sectors instrumental in the ongoing decarbonizationprocess. However, wind turbines are subjected to a wide range of dynamic loads which can cause more frequentfailures and downtime periods, leading to ever-increasing attention to effective Condition Monitoring *** this paper, we propose a novel unsupervised deep anomaly detection framework to detect anomalies in windturbines based on SCADA data. We introduce a promising neural architecture, namely a graph convolutionalautoencoder for Multivariate Time series, to model the sensor network as a dynamical functional graph. Thisstructure improves the unsupervised learning capabilities of autoencoders by considering individual sensormeasurements together with the nonlinear correlations existing among signals. On this basis, we developeda deep anomaly detection framework that was validated on 12 failure events occurred during 20 months ofoperation of four wind turbines. The results show that the proposed framework successfully detects anomaliesand anticipates SCADA alarms by outperforming other two recent neural approaches.

关键词： Wind turbine Condition monitoring Deep anomaly detection SCADA data graph convolutional autoencoder Multivariate Time series Early fault detection

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A novel intelligent multicross domain fault diagnosis of servo motor-bearing system based on Domain Generalized graph Convolution autoencoder

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2025年第3期24卷 1485-1499页

作者： Zhao, Xiaoli Hu, Yuanhao Liu, Jiahui Yao, Jianyong Deng, Wenxiang Hu, Jian Zhao, Zhuanzhe Yan, Xiaoan Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China Chongqing Univ State Key Lab Mech Transmission Adv Equipment Chongqing Peoples R China Anhui Polytech Univ Sch Artificial Intelligence Wuhu Peoples R China Nanjing Forestry Univ Sch Mechatron Engn Nanjing Peoples R China

The data measured by the servo motor-bearing system under complex working conditions will present problems such as amplitude fluctuations, unequal impact intervals, and significant differences in data distribution, and so forth. However, the most intelligent fault diagnosis focus on deep learning or transfer learning, which cannot complement knowledge transfer and generalized diagnosis with the structural neighbor relationship under unknown conditions or cross-machine samples. By utilizing Domain Generalized graph Convolution autoencoder (DGGCAE), a novel intelligent multicross domain fault diagnosis method for servo-motor bearing systems can be developed. Specifically, the Dirichlet Mixup and Distilled augmentations are first employed to augment the domain data of the feature and label layer for model training. Accordingly, graph representation learning on multisource domain data is mainly performed for the developed algorithm. Afterward, the graph convolutional autoencoder is employed to extract enough generalized high-dimensional features. Furthermore, DGGCAE's classification loss and domain discrimination loss can be calculated to narrow the distribution gap among multisource domains. Finally, the fault simulation test bench (called servo motor-Cylindrical roller bearing system from Nanjing University of Science and Technology) has validated the development of the diagnostic method.

关键词： Servo motor system cylindrical roller bearing multicross domain domain generalization graph convolutional autoencoder

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