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Predicting latent lncRNA and cancer metastatic event associations via variational graph auto-encoder

METHODS 2023年第1期211卷 1-9页

作者： Zhu, Yuan Zhang, Feng Zhang, Shihua Yi, Ming China Univ Geosci Sch Automat 388 Lumo Rd Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co 388 Lumo Rd Wuhan 430074 Hubei Peoples R China Engn Res Ctr Intelligent Technol Geoexplorat 388 Lumo Rd Wuhan 430074 Hubei Peoples R China China Univ Geosci Sch Math & Phys 388 Lumo Rd Wuhan 430074 Hubei Peoples R China Wuhan Univ Sci & Technol Coll Life Sci & Hlth 974 Heping Ave Wuhan 430081 Hubei Peoples R China

Long non-coding RNA (lncRNA) are shown to be closely associated with cancer metastatic events (CME, e.g., cancer cell invasion, intravasation, extravasation, proliferation) that collaboratively accelerate malignant cancer spread and cause high mortality rate in patients. Clinical trials may accurately uncover the relationships between lncRNAs and CMEs;however, it is time-consuming and expensive. With the accumulation of data, there is an urgent need to find efficient ways to identify these relationships. Herein, a graph embedding representation-based predictor (VGEA-LCME) for exploring latent lncRNA-CME associations is introduced. In VGEA-LCME, a heterogeneous combined network is constructed by integrating similarity and linkage matrix that can maintain internal and external characteristics of networks, and a variational graph auto-encoder serves as a feature generator to represent arbitrary lncRNA and CME pair. The final robustness predicted result is obtained by ensemble classifier strategy via cross-validation. Experimental comparisons and literature verification show better remarkable performance of VGEA-LCME, although the similarities between CMEs are challenging to calculate. In addition, VGEA-LCME can further identify organ-specific CMEs. To the best of our knowledge, this is the first computational attempt to discover the potential relationships between lncRNAs and CMEs. It may provide support and new insight for guiding experimental research of metastatic cancers. The source code and data are available at https://github .com /zhuyuan -cug /VGAE-LCME.

关键词： lncRNA Cancer metastatic events variational graph auto-encoder Interaction prediction

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LIGHT FIELD COMPRESSION VIA A variational graph auto-encoder 18

LIGHT FIELD COMPRESSION VIA A VARIATIONAL GRAPH AUTO-ENCODER

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International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Teng, Wenjun Li, Yong Kwong, Sam City Univ Hong Kong Dept Comp Sci Kowloon 83 Tat Chee Ave Hong Kong Peoples R China

ISBN: (纸本)9781665466110

Massive light field (LF) data bring tremendous storage and transmission challenges, making the LF compression scheme highly demanded. This paper proposes a novel LF compression method via a variational graph auto-encoder (VGAE), aiming to exploit better the structural information of edges and vertices of the graph LF image. More specifically, the graph adjacency matrix and feature matrix are derived from the original graph data in the encoder. Subsequently, a graph convolutional network (GCN) is utilized to determine a multi-dimensional Gaussian distribution, from which the latent representation can be derived by sampling. Finally, the graph LF image can be reconstructed by the inner product of the latent variable in the decoder. The distinct characteristics of the proposed scheme lie in that VGAE encoder applies GCN as a function, which can better alleviate the loss of compression. Moreover, the divergence between the original and the reconstructed signals is evaluated using KL divergence to ensure that the estimator is unbiased, leading to better adaptability. The experimental results demonstrate that the proposed method achieves better performance than the state-of-the-art methods.

关键词： Light field compression variational graph auto-encoder graph convolutional network

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variational graph neural network with diffusion prior for link prediction

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APPLIED INTELLIGENCE 2025年第2期55卷 1-14页

作者： Su, Hailong Li, Zhipeng Yuan, Chang-An Vladimir, F. Filaretov Huang, De-Shuang Tongji Univ Sch Elect & Informat Engn Shanghai 200082 Peoples R China Univ Sci & Technol China Hefei 230000 Anhui Peoples R China Guangxi Acad Sci Inst Big Data Nanning 530000 Guangxi Peoples R China Guangxi Acad Sci Intelligent Comp Res Ctr Nanning 530000 Guangxi Peoples R China Russian Acad Sci Inst Automat & Control Proc Far Eastern Branch Vladivostok 690041 Russia Ningbo Inst Digital Twin Eastern Inst Technol Ningbo 315201 Zhejiang Peoples R China Tongji Univ Med Innovat Ctr Inst Regenerat Med Shanghai 200123 Peoples R China Tongji Univ Shanghai East Hosp Sch Life Sci & Technol State Key Lab Cardiol Shanghai 200123 Peoples R China

Recently, graph neural networks(GNNs) has achieved tremendous success in a variety of fields. Many approaches have been proposed to address data with graph structure. However, many of these are deterministic methods, therefore, they are unable to capture the uncertainty, which is inherent in the nature of graph data. Various VAE(variational auto-encoder)-based approaches have been proposed to tackle such problems. Unfortunately, due to the simple a posterior and a prior assumption problems of such methods, they are not well suited to handle uncertainty in graph data. For example, VGAE(variational graph auto-encoder) assumes that the posterior and prior distributions are simple Gaussian distributions, which can lead to overfitting problems when incompatible with the true distributions. Many methods propose to solve the posterior distribution problem, but most ignore the effect of the prior distribution. Therefore, in this paper, we proposed a novel method to solve the Gaussian prior problem. Specifically, in order to enhance the representation power of the prior distribution, we use the diffusion model to model the prior distribution. We incorporate the diffusion model into VGAE. In the forward diffusion process, noise is gradually added to the latent variables, and then the samples are recovered by the backward diffusion process. To realize the backward diffusion process, we propose a new denoising model which predicts noise by stacking GCN(graph Convolution Network) and MLP(Multi-layers Perceptron). We perform experiments on different datasets and the experimental results demonstrate that our method obtains state-of-the-art results.

关键词： graph neural network variational graph auto-encoder Diffusion model Denoising process Prior distribution

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DyVGRNN: DYnamic mixture variational graph Recurrent Neural Networks

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NEURAL NETWORKS 2023年第1期165卷 596-610页

作者： Niknam, Ghazaleh Molaei, Soheila Zare, Hadi Pan, Shirui Jalili, Mahdi Zhu, Tingting Clifton, David Univ Tehran Dept Data Sci & Technol Tehran Iran Univ Oxford Dept Engn Sci Oxford England Griffith Univ Sch Informat & Commun Technol Nathan Qld Australia RMIT Univ Sch Engn Bundoora Vic Australia Oxford Suzhou Ctr Adv Res OSCAR Suzhou Peoples R China

Although graph representation learning has been studied extensively in static graph settings, dynamic graphs are less investigated in this context. This paper proposes a novel integrated variational framework called DYnamic mixture variational graph Recurrent Neural Networks (DyVGRNN), which consists of extra latent random variables in structural and temporal modelling. Our proposed framework comprises an integration of variational graph auto-encoder (VGAE) and graph Recurrent Neural Network (GRNN) by exploiting a novel attention mechanism. The Gaussian Mixture Model (GMM) and the VGAE framework are combined in DyVGRNN to model the multimodal nature of data, which enhances performance. To consider the significance of time steps, our proposed method incorporates an attention-based module. The experimental results demonstrate that our method greatly outperforms state-of-the-art dynamic graph representation learning methods in terms of link prediction and clustering.1 & COPY;2023 Elsevier Ltd. All rights reserved.

关键词： Dynamic graph representation learning Dynamic node embedding variational graph auto-encoder graph recurrent neural network Attention mechanism

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Predicting potential interactions between lncRNAs and proteins via combined graph auto -encoder methods

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BRIEFINGS IN BIOINFORMATICS 2023年第1期24卷 bbac527-bbac527页

作者： Zhao, Jingxuan Sun, Jianqiang Shuai, Stella C. Zhao, Qi Shuai, Jianwei Univ Sci & Technol Liaoning Anshan Peoples R China Linyi Univ Linyi Peoples R China Northwestern Univ Evanston IL USA Xiamen Univ Dept Phys Xiamen Peoples R China

Long noncoding RNA (lncRNA) is a kind of noncoding RNA with a length of more than 200 nucleotide units. Numerous research studies have proven that although lncRNAs cannot be directly translated into proteins, lncRNAs still play an important role in human growth processes by interacting with proteins. Since traditional biological experiments often require a lot of time and material costs to explore potential lncRNA-protein interactions (LPI), several computational models have been proposed for this task. In this study, we introduce a novel deep learning method known as combined graph auto -encoders (LPICGAE) to predict potential human LPIs. First, we apply a variational graph auto -encoder to learn the low dimensional representations from the high -dimensional features of lncRNAs and proteins. Then the graph auto -encoder is used to reconstruct the adjacency matrix for inferring potential interactions between lncRNAs and proteins. Finally, we minimize the loss of the two processes alternately to gain the final predicted interaction matrix. The result in 5 -fold cross -validation experiments illustrates that our method achieves an average area under receiver operating characteristic curve of 0.974 and an average accuracy of 0.985, which is better than those of existing six state-of-the-art computational methods. We believe that LPICGAE can help researchers to gain more potential relationships between lncRNAs and proteins effectively.

关键词： LncRNA protein lncRNA-protein interactions graph auto -encoder variational graph auto-encoder

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DefenseVGAE: Defending Against Adversarial Attacks on graph Data via a variational graph autoencoder 20th

DefenseVGAE: Defending Against Adversarial Attacks on Graph ...

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20th International Conference on Intelligent Computing (ICIC)

作者： Zhang, Ao Ma, Jinwen Peking Univ Sch Math Sci Dept Informat & Computat Sci Beijing 100871 Peoples R China

ISBN: (纸本)9789819755905;9789819755912

graph neural networks (GNNs) achieve remarkable performances for the tasks on graph data. However, recent studies uncover that they are extremely vulnerable to adversarial structural perturbations, leading to their outcomes unreliable. In this paper, we propose DefenseVGAE, a novel defense method for lever-aging variational graph autoencoders (VGAEs) to defend GNNs against such attacks. Specifically, DefenseVGAE is trained to reconstruct the graph structure of the graph data in which the reconstructed adjacency matrix is sparse and can reduce the effects of adversarial perturbations and boost the performance of GCN when facing the adversarial attacks. Our experiments on three typical datasets demonstrate that DefenseVGAE is effective under various threat models and even outperforms the existing defense strategies in certain settings.

关键词： graph Neural Networks Adversarial Defense variational graph auto-encoder

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End-to-end variational graph clustering with local structural preservation

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NEURAL COMPUTING & APPLICATIONS 2022年第5期34卷 3767-3782页

作者： Guo, Lin Dai, Qun Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China

graph clustering, a basic problem in machine learning and artificial intelligence, facilitates a variety of real-world applications. How to perform a task of graph clustering, with a relatively high-quality optimization decision and an effective yet efficient way to use graph information, to obtain a more excellent assignment for discrete points is not an ordinary challenge that troubles scholars. Often, many preeminent works on graph clustering neglect an essential element that the defined clustering loss may destroy the feature space. This is also a vital factor that leads to unrepresentative nonsense features that generate poor partitioning decisions. Here, we propose an end-to-end variational graph clustering (EVGC) algorithm focusing on preserving the original information of the graph. Specifically, the KL loss with an auxiliary distribution serves as a specific guide to manipulate the embedding space, and consequently disperse data points. A graph auto-encoder plays a propulsive role in maximumly retaining the local structure of the generative distribution of the graph. And each node is represented as a Gaussian distribution in dealing with separating the true embedding position and uncertainty from the graph. Experimental results reveal the importance of preserving local structure, and our EVGC system outperforms state-of-the-art approaches.

关键词： graph convolutional neural network variational graph embedding graph clustering variational graph auto-encoder

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graph Convolutional auto-encoders for Predicting Novel lncRNA-Disease Associations

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第4期19卷 2264-2271页

作者： Silva, Ana B. O., V Spinosa, E. J. Univ Fed Parana BR-80060000 Curitiba Parana Brazil

LncRNAs are intermediate molecules that participate in the most diverse biological processes in humans, such as gene expression control and X-chromosome inactivation. Numerous researches have associated lncRNAs with a wide range of diseases, such as breast cancer, leukemia, and many other conditions. In this work, we propose a graph-based method named PANDA. This method treats the prediction of new associations between lncRNAs and diseases as a link prediction problem in a graph. We start by building a heterogeneous graph that contains the known associations between lncRNAs and diseases and additional information such as gene expression levels and symptoms of diseases. We then use a graph auto-encoder to learn the representation of the nodes' features and edges, finally applying a Neural Network to predict potentially interesting novel edges. The experimental results indicate that PANDA achieved a 0.976 AUC-ROC, surpassing state-of-the-art methods for the same problem, showing that PANDA could be a promising approach to generate embeddings to predict potentially novel lncRNA-disease associations.

关键词： Diseases Databases Prediction algorithms Gene expression Encoding Bioinformatics RNA lncRNA-disease association prediction link prediction in graphs variational graph auto-encoder

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variational graph autoencoder with Adversarial Mutual Information Learning for Network Representation Learning

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2022年第3期17卷 1-18页

作者： Li, Dongjie Li, Dong Lian, Guang South China Univ Technol Guangzhou Peoples R China

With the success of graph Neural Network (GNN) in network data, some GNN-based representation learning methods for networks have emerged recently. variational graph autoencoder (VGAE) is a basic GNN framework for network representation. Its purpose is to well preserve the topology and node attribute information of the network to learn node representation, but it only reconstructs network topology, and does not consider the reconstruction of node features. This strategy will make node representation can not well reserve node features information, impairing the ability of the VGAE method to learn higher quality representations. To solve this problem, we arise a new network representation method to improve the VGAE method for well retaining both node features and network structure information. The method utilizes adversarial mutual information learning to maximize the mutual information (MI) of node features and node representations during the encoding process of the variational autoencoder, which forces the variational encoder to get the representation containing the most informative node features. The method consists of three parts: a variational graph autoencoder includes a variational encoder (MI generator (G)) and a decoder, a positive MI sample module (maximizing MI module), and an MI discriminator (D). Furthermore, we explain why maximizing MI between node features and node representation can reconstruct node attributes. Finally, we conduct experiments on seven public representative datasets for nodes classification, nodes clustering, and graph visualization tasks. Experimental results demonstrate that the proposed algorithm significantly outperforms current popular network representation algorithms on these tasks. The best improvement is 17.13% than the VGAE method.

关键词： Network representation variational graph auto-encoder adversarial learning mutual information maximization

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Unsupervised spatially embedded deep representation of spatial transcriptomics

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GENOME MEDICINE 2024年第1期16卷 1-15页

作者： Xu, Hang Fu, Huazhu Long, Yahui Ang, Kok Siong Sethi, Raman Chong, Kelvin Li, Mengwei Uddamvathanak, Rom Lee, Hong Kai Ling, Jingjing Chen, Ao Shao, Ling Liu, Longqi Chen, Jinmiao ASTAR Singapore Immunol Network SIgN Singapore 138648 Singapore ASTAR Inst High Performance Comp IHPC Singapore 138632 Singapore BGI BGI Res Southwest Chongqing 401329 Peoples R China Jinfeng Lab JFL BGI STOm Ctr Chongqing 401329 Peoples R China Univ Chinese Acad Sci UCAS Terminus AI Lab Beijing Peoples R China BGI ShenZhen Shenzhen 518103 Peoples R China Natl Univ Singapore NUS Yong Loo Lin Sch Med Dept Microbiol & Immunol Immunol Translat Res Program 5 Sci Dr 2BlkMD4Level 3 Singapore 117545 Singapore

Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled with a masked self-supervised learning mechanism to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. SEDR achieved higher clustering performance on manually annotated 10 x Visium datasets and better scalability on high-resolution spatial transcriptomics datasets than existing methods. Additionally, we show SEDR's ability to impute and denoise gene expression (URL: https://***/JinmiaoChenLab/SEDR/).

关键词： Spatial transcriptomics Spatial clustering variational graph auto-encoder Batch integration Trajectory inference Gene imputation

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