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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jiang, Kang Liao, Bo. Papagerakis, Petros Wu, Fang-Xiang University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada Hainan Normal University School of Mathematics and Statistics Haikou China University of Saskatchewan College of Dentistry Saskatoon Canada University of Saskatchewan Division of Biomedical Engineering Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

Single-cell RNA-sequencing (scRNA-seq) technology has revolutionized the field by enabling the profiling of transcriptomes in cell resolution. However, it is flawed by the sparsity caused by low mRNA capture efficiency during sequencing. This results in "dropout"events where genes are expressed but not detected. Dropout can hinder downstream analyses like differential expression and clustering. To tackle this issue, we present a novel imputation approach called MKGAE, which utilizes graph convolution and autoencoder techniques to construct a generative model for imputing missing values within scRNA-seq data. Meanwhile, considering the intricate relationships between genes, merging them into a single graph might lead to the loss of important insights. To address this, we utilize two gene-to-gene graph kernels for graph convolution. Experiments across both simulated and real scRNA-seq datasets illustrate MKGAE's superiority over other state-of-the-art methods in terms of clustering analysis and differentially expressed gene identification. © 2023 IEEE.

关键词： graph autoencoder Imputation Single-cell RNA-seq

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Enhancing equipment safeguarding in IIoT: A self-supervised fault diagnosis paradigm based on asymmetric graph autoencoder

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KNOWLEDGE-BASED SYSTEMS 2024年 296卷

作者： Chen, Zhuohang Liu, Shen Li, Chao Chang, Yuanhong Chen, Jinglong Feng, Gaoshan He, Shuilong Xi An Jiao Tong Univ State Key Lab Mfg & Syst Engn Xian 710049 Peoples R China Dongfeng Liuzhou Motor Co Ltd Liuzhou 545005 Peoples R China Guilin Univ Elect Technol Sch Mech & Elect Engn Guilin 541004 Peoples R China

Thanks to the sufficient monitoring data provided by Industrial Internet of Things (IIoT), intelligent fault diagnosis technology has demonstrated remarkable performance in safeguarding equipment. However, the effectiveness of existing methods heavily relies on manually labeled data. Unfortunately, data collected from equipment often lacks labels, leading to a scarcity of fault data. Furthermore, an additional significant challenge is the feature domain shift resulting from speed variation. To address this, we propose a self-supervised paradigm based on an asymmetric graph autoencoder for fault diagnosis under domain shift, aiming to mine valuable health information from unlabeled data. Unlike Euclidean-based methods, the proposed method transforms time series samples into graphs and extracts domain invariant features through information interaction between nodes. To efficiently mine unlabeled data and enhance generalization, the self-supervised learning paradigm utilizes an asymmetric graph autoencoder architecture. This architecture includes an encoder that learns selfsupervised representations from unlabeled samples and a lightweight decoder that predicts the original input. Specifically, we mask a portion of input samples and predict the original input from learned self-supervised representations. In downstream task, the pre-trained encoder is fine-tuned using limited labeled data for specific fault diagnosis task. The proposed method is evaluated on three mechanical fault simulation experiments, and the results demonstrate the its superiority and potential.

关键词： Self -supervised learning graph autoencoder Fault diagnosis Domain shift

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Dual-decoder graph autoencoder for unsupervised graph representation learning

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KNOWLEDGE-BASED SYSTEMS 2021年 234卷 1页

作者： Sun, Dengdi Li, Dashuang Ding, Zhuanlian Zhang, Xingyi Tang, Jin Anhui Univ Sch Artificial Intelligence Key Lab Intelligent Comp & Signal Proc ICSP Minist Educ Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Anhui Prov Key Lab Multimodal Cognit Comp Hefei 230601 Peoples R China Anhui Univ Sch Internet Hefei 230039 Peoples R China

Unsupervised graph representation learning is a challenging task that embeds graph data into a low dimensional space without label guidance. Recently, graph autoencoders have been proven to be an effective way to solve this problem in some attributed networks. However, most existing graph autoencoder-based embedding algorithms only reconstruct the feature maps of nodes or the affinity matrix but do not fully leverage the latent information encoded in the low-dimensional representation. In this study, we propose a dual-decoder graph autoencoder model for attributed graph embedding. The proposed framework embeds the graph topological structure and node attributes into a compact representation, and then the two decoders are trained to reconstruct the node attributes and graph structures simultaneously. The experimental results on clustering and link prediction tasks strongly support the conclusion that the proposed model outperforms the state-of-the-art approaches. (c) 2021 Elsevier B.V. All rights reserved.

关键词： graph clustering graph autoencoder graph representation learning graph neural networks graph embedding

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Community hiding using a graph autoencoder

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KNOWLEDGE-BASED SYSTEMS 2022年 253卷

作者： Liu, Dong Chang, Zhengchao Yang, Guoliang Chen, Enhong Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453000 Henan Peoples R China Key Lab Artificial Intelligence & Personalized Le Xinxiang 453000 Henan Peoples R China Big Data Engn Lab Teaching Resources & Assessment Xinxiang Henan Peoples R China Univ Sci & Technol China Sch Comp Sci Hefei 230000 Anhui Peoples R China

Community detection can reveal real social relations and enable great economic benefits for enterprises and organizations;however, it can also cause privacy problems such as the disclosure of individual or group information amongst community members, which goes against the hidden wishes of individuals and groups. Therefore, community hiding has received increasingly more attention in recent years. However, the network generation mechanism has not been considered in previous studies on community hiding. Generation models can reflect the generation process of the network and show the strength of the connection between nodes. To this end, we propose a new graph autoencoder for the community hiding algorithm, namely, GCH, which not only hides the community structure but also embodies the generation mechanism of the network. It uses the rules of the generation process from underfitting to overfitting in the community network to select the connections that have the greatest impact on the community structure for rewiring. After analyzing the essence of community detection algorithms and graph neural networks, an improved graph autoencoder is used to reconstruct the probabilistic adjacency matrix;and under the constraint of an "invisible perturbation" of the network structure, the existing mainstream community detection algorithm is attacked, which greatly reduces the accuracy of community detection results. For the verification of model effectiveness, two widely used indicators NMI and AE are used to compare the performance of our attack on the community detection algorithm with other baselines under different dimension settings. Compared with several baseline algorithms, extensive experimental results are obtained. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Community detection Community hiding graph autoencoder Adversarial attack

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A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations

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BMC BIOINFORMATICS 2021年第1期22卷 136-136页

作者： Shi, Zhuangwei Zhang, Han Jin, Chen Quan, Xiongwen Yin, Yanbin Nankai Univ Coll Artificial Intelligence Tongyan Rd Tianjin 300350 Peoples R China Nankai Univ Coll Comp Sci Tongyan Rd Tianjin 300350 Peoples R China Univ Nebraska Nebraska Food Hlth Ctr Dept Food Sci & Technol 1400 R St Lincoln NE 68588 USA

Background: Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNA-disease associations for disease prognosis, diagnosis and therapy. Dozens of machine learning and deep learning algorithms have been adopted to this problem, yet it is still challenging to learn efficient low-dimensional representations from high-dimensional features of lncRNAs and diseases to predict unknown lncRNA-disease associations accurately. Results: We proposed an end-to-end model, VGAELDA, which integrates variational inference and graph autoencoders for lncRNA-disease associations prediction. VGAELDA contains two kinds of graph autoencoders. Variational graph autoencoders (VGAE) infer representations from features of lncRNAs and diseases respectively, while graph autoencoders propagate labels via known lncRNA-disease associations. These two kinds of autoencoders are trained alternately by adopting variational expectation maximization algorithm. The integration of both the VGAE for graph representation learning, and the alternate training via variational inference, strengthens the capability of VGAELDA to capture efficient low-dimensional representations from high-dimensional features, and hence promotes the robustness and preciseness for predicting unknown lncRNA-disease associations. Further analysis illuminates that the designed co-training framework of lncRNA and disease for VGAELDA solves a geometric matrix completion problem for capturing efficient low-dimensional representations via a deep learning approach. Conclusion: Cross validations and numerical experiments illustrate that VGAELDA outperforms the current state-of-the-art methods in lncRNA-disease association prediction. Case studies indicate that VGAELDA is capable of detecting potential lncRNA-disease associations. The source code and data are available at https://***/zhanglabNKU/VGAELDA.

关键词： Variational inference graph autoencoder lncRNA-disease association Representation learning

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GGAECDA: Predicting circRNA-disease associations using graph autoencoder based on graph representation learning

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2022年第0期99卷 107722页

作者： Li, Guanghui Lin, Yawei Luo, Jiawei Xiao, Qiu Liang, Cheng East China Jiaotong Univ Sch Informat Engn Nanchang Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China Hunan Normal Univ Coll Informat Sci & Engn Changsha Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan Peoples R China

Numerous studies have shown that circular RNAs (circRNAs) can serve as ideal disease markers as they are involved in most cellular activities of organisms and are key regulators in various pathological processes. Therefore, the association analysis of circRNAs and diseases can explore the role of circRNAs in diseases and provide help for practical medical research. However, the traditional biotechnology are not convenient for identifying unconfirmed interactions between circRNAs and diseases, which need too many resources and long experimental period. In this work, a new deep learning model is advanced, which is based on graph autoencoder (GAE) constructed with graph attention network (GAT) and random walk with restart (RWR) for predicting circRNA-disease associations (GGAECDA). In detail, GAT is designed to learn the hidden representations of circRNAs and diseases through using low-order neighbor information from circRNA similarity network and disease similarity network respectively, while RWR is employed to learn the latent features of circRNAs and diseases via using high-order neighbor information from the same two networks respectively. After that, these two parts of features of circRNAs and diseases are combined to form new feature representations of circRNAs and diseases respectively. Finally, two GAEs are constructed for co-training to fully integrate information from circRNA space and disease space and calculate potential association prediction scores. Unlike previous models, GGAECDA deeply mines low-dimensional representations from node similarity network through using GAT and RWR. The average AUC value obtained from GGAECDA with a five-fold cross-validation result is 0.9359. Furthermore, case studies demonstrate the ability of GGAECDA to detect potential candidate circRNAs for human diseases. The above results show that the GGAECDA model can be used as a reliable tool to guide subsequent studies on the regulatory functions of circRNAs.

关键词： CircRNA-disease associations graph attention network Random walk with restart graph autoencoder

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A2AE: Towards adaptive multi-view graph representation learning via all-to-all graph autoencoder architecture

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APPLIED SOFT COMPUTING 2022年第0期125卷

The multi-view graph is a fundamental data model, which is used to describe complex networks in the real world. Learning the representation of multi-view graphs is a vital step for understanding complex systems and extracting knowledge accurately. However, most existing methods focus on a certain view or simply add multiple views together, which prevents them from making the best of the rich relational information in multiple views and ignores the importance of different views. In this paper, a novel all-to-all graph autoencoder is proposed for multi-view graph representation learning, namely A2AE. The all-to-all model first embeds the attribute multi-view graph into compact representations by semantic fusing the view-specific compact representations from multi-encoders, and then multi -decoders are trained to reconstruct graph structure and attributes. Finally, a self-training clustering module is attached for clustering tasks.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Multi -view graph graph autoencoder Network embedding graph neural networks

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Remaining useful life prediction of lithium battery based on multi decoder graph autoencoder and transformer network

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IFAC-PapersOnLine 2024年第29期58卷 350-355页

作者： Yan Ma Jiaqi Li Jinwu Gao Department of Control Science and Engineering Jilin University Changchun 130012 China

Remaining useful life (RUL) of lithium-ion battery is important to maintain safe and reliable battery operation. Health indicators (HIs) are key features for predicting RUL during battery aging, whereas current methods only consider their link to capacity. In order to learn the intrinsic connection between the aging features, a RUL prediction method based on multi decoder graph autoencoder (MGAE) and transformer network is proposed, which considers both the link between aging characteristics and the link between aging characteristics and capacity degradation. First, multiple types of aging features are extracted during battery charging and discharging, and HIs are connected into a graph structure by pearson correlation analysis. Thereby, feature information with high correlation is linked through the topology of the graph. Subsequently, the feature graph and feature matrix are input to the graph autoencoder to extract deep features. In graph decoder part, this paper improves to a multi decoder in order to update and select features by the updated graph structure. Finally, new feature matrix is fed into transformer, and RUL prediction is realized by parallel processing through multi-head self-attention. The validity of proposed method is demonstrated by NASA dataset and compared with other advanced methods. The results show that our approach achieves average RE of 0.09 and maintains RMSE of 0.01.

关键词： Lithium-ion Battery Remaining Useful Life graph autoencoder Multi Decoder Transformer Network

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Social network node pricing based on graph autoencoder in data marketplaces

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 243卷

作者： Sun, Yongjiao Li, Boyang Bi, Xin Feng, Qiang Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Northeastern Univ Key Lab Minist Educ Safe Min Deep Met Mines Shenyang 110819 Peoples R China BIT Tangshan Res Inst Tangshan 063000 Peoples R China Hebei Prov Key Lab Big Data Sci & Intelligent Tech Tangshan 063000 Peoples R China

Data have become a valuable digital resource. It has in turn precipitated the emergence of big data marketplaces. For social network date in the marketplaces, each node should be priced according to its influence. The key challenge is that deep learning based pricing models require initial cascade graphs as inputs to predict influence, which cannot be obtained while pricing nodes. Furthermore, node pricing must enhance purchase intentions while being consistent with their influence. To address these challenges, a nodepricing framework is proposed, in which market price is determined based on the predicted influence. In this framework, corrections are performed by using a graph autoencoder (GAE). The corrections are used to augment the neighborhood subgraph and facilitate the extraction of valid sequence features, which are then used to predict influence. An approximate Shapley value for node influence is used to evaluate the price of the nodes. A multi -perspective pricing approach is further investigated, where consumer utility and the approximate Shapley value for influence are the objectives. An inflection point is chosen on the Pareto frontier to select a price that enhances consumer utility. Extensive experiments were conducted on two real -world social network datasets. The results indicate that our performance is higher than DeepCas by 10.38% in Twitter and 9.64% in Weibo . The price output by our framework is consistent with the nodes' social marketing value while maximizing consumer utility.

关键词： Data pricing Social network graph autoencoder Influence prediction LSTM

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GLASS: A graph Laplacian autoencoder with Subspace Clustering Regularization for graph Clustering

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COGNITIVE COMPUTATION 2023年第3期15卷 803-821页

作者： Sun, Dengdi Liu, Liang Luo, Bin Ding, Zhuanlian Anhui Univ Sch Artificial Intelligence Key Lab Intelligent Comp & Signal Proc ICSP Minist Educ Hefei 230601 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230026 Peoples R China Anhui Univ Sch Comp Sci & Technol Anhui Prov Key Lab Multimodal Cognit Comp Hefei 230601 Peoples R China Anhui Univ Sch Internet Hefei 230039 Peoples R China

graph clustering is an important unsupervised learning task in complex network analysis and its latest progress mainly relies on a graph autoencoder (GAE) model. However, these methods have three major drawbacks. (1) Most autoencoder models choose graph convolutional networks (GCNs) as their encoders, but the filters and weight matrices in GCN encoders are entangled, which affects the resulting representation performance. (2) Real graphs are often sparse, requiring multiple-layer propagation to generate effective features, but (GCN) encoders are prone to oversmoothing when multiple layers are stacked. (3) Existing methods ignore the distribution of the node features in the feature space during the embedding stage, making their results unsuitable for clustering tasks. To alleviate these problems, in this paper, we propose a novel graph Laplacian autoencoder with subspace clustering regularization for graph clustering (GLASS). Specifically, we first use Laplacian smoothing filters instead of GCNs for feature propagation and multilayer perceptrons (MLPs) for nonlinear transformations, thereby solving the entanglement between convolutional filters and weight matrices. Considering that multilayer propagation is prone to oversmoothing, we further add residual connections between the Laplacian smoothing filters to enhance the multilayer feature propagation capability of GLASS. In addition, to achieve improved clustering performance, we introduce a regular term for subspace clustering to constrain the autoencoder to obtain the node features that are more representative and suitable for clustering. Experiments on node clustering and image clustering using four widely used network datasets and three image datasets show that our method outperforms other existing state-of-the-art methods. In addition, we verify the effectiveness of the proposed method in link prediction, complexity analysis, parameter analysis, data visualization, and ablation studies. The experimental results

关键词： graph clustering graph autoencoder graph neural networks graph representation learning

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