Time Series Anomaly Detection Based on Graph Convolutional Networks

Hu, Zhiwei; Wu, Tao; Zhang, Yunan; Li, Jintao; Jiang, Longsheng

doi:10.1109/icaml51583.2020.00036

Cited by 154 publications

(234 citation statements)

References 19 publications

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“…L-layer GCN has been proven to essentially simulate an L-order polynomial filter with fixed coefficients, which limits the expressive power of the model and leads to over-smoothing (Wu, et al, 2019). As a result, GCN achieves its best performance in many graph-based problems via shallow architectures, which are incapable of extracting information from highorder neighbors.…”

Section: Gcn With Initial Residual and Identity Mappingmentioning

confidence: 99%

Structure-aware protein–protein interaction site prediction using deep graph convolutional network

et al. 2021

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Motivation Protein-protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information. Results We propose a deep graph-based framework GraphPPIS (deep Graph convolutional network for Protein-Protein Interacting Site prediction) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to 8 layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction. Availability The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Gcn With Initial Residual and Identity Mappingmentioning

confidence: 99%

Structure-aware protein–protein interaction site prediction using deep graph convolutional network

et al. 2021

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“…Avenues for future improvement divert focus from a deep architecture that chains 1-hop convolutional layers to an alternative strategy that aggregates outputs from multiple shallow networks whose convolutions encode richer, multihop diffusion operators. 28,29 To sustain performance for different depth limits, jumping knowledge concatenation was necessary. For model variants that do not incorporate layer aggregation, decreased performance for depth limit two networks that may result from oversmoothing, whereby node hidden states converge to an almost uniform distribution and local neighborhood information is lost.…”

Section: T H Imentioning

confidence: 99%

Message Passing Neural Networks Improve Prediction of Metabolite Authenticity

Flynn

Swamidass

2023

J. Chem. Inf. Model.

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Cytochrome P450 enzymes aid in the elimination of a preponderance of small molecule drugs, but can generate reactive metabolites that may adversely react with protein and DNA and prompt drug candidate attrition or market withdrawal. Previously developed models help understand how these enzymes modify molecule structure by predicting sites of metabolism or characterizing formation of metabolite-biomolecule adducts. However, the majority of reactive metabolites are formed by multiple metabolic steps, and understanding the progenitor molecule's network-level behavior necessitates an integrative approach that blends multiple site of metabolism and structure inference models. Our previously developed tool, XenoNet 1.0, generates metabolic networks, where nodes are molecules and weighted edges are metabolic transformations. We extend XenoNet with a bidirectional message passing neural network that integrates edge feature information and local network structure using edge-conditioned graph convolutions and jumping knowledge to predict the authenticity of inferred Phase I metabolite structures. Our model significantly outperformed prior work and algorithmic baselines on a data set of 311 networks and 6606 intermediates annotated using a chemically diverse set of 20 736 individual in vitro and in vivo reaction records accounting for 92.3% of all human Phase I metabolism in the Accelrys Metabolite Database. Cross-validated predictions resulted in area under the receiver operating characteristic curves of 88.5% and 87.6% for separating experimentally observed and unobserved metabolites at global and network levels, respectively. Further analysis verified robustness to networks of varying depth and breadth, accurate detection of metabolites, such as D,L-methamphetamine, that are experimentally observed or unobserved in different network contexts, extraction of important metabolic subnetworks, and identification of known bioactivation pathways, such as for nimesulide and terbinafine. By exploiting network structures, our approach accurately suggests unreported metabolites for experimental study and may rationalize modifications for avoiding deleterious pathways antecedent to reactive metabolite formation.

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“…And the rest of the graph kernel methods that are usually used in graph classification are employed in the following comparison of the graph classification task. Aside from unsupervised approaches, we also use five popular semisupervised methods for comparison, including Planetoid [6], Chebyshev [44], GCN [20], SGC [40], and GAT [36].…”

Section: Baselinesmentioning

confidence: 99%

SimGRL: a simple self-supervised graph representation learning framework via triplets

Huang

Lei

Zeng

2023

Complex Intell. Syst.

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Recently, graph contrastive learning (GCL) has achieved remarkable performance in graph representation learning. However, existing GCL methods usually follow a dual-channel encoder network (i.e., Siamese networks), which adds to the complexity of the network architecture. Additionally, these methods overly depend on varied data augmentation techniques, corrupting graph information. Furthermore, they are heavily reliant on large quantities of negative nodes for each object node, which requires tremendous memory costs. To address these issues, we propose a novel and simple graph representation learning framework, named SimGRL. Firstly, our proposed network architecture only contains one encoder based on a graph neural network instead of a dual-channel encoder, which simplifies the network architecture. Then we introduce a distributor to generate triplets to obtain the contrastive views between nodes and their neighbors, avoiding the need for data augmentations. Finally, we design a triplet loss based on adjacency information in graphs that utilizes only one negative node for each object node, reducing memory overhead significantly. Extensive experiments demonstrate that SimGRL achieves competitive performance on node classification and graph classification tasks, especially in terms of running time and memory overhead.

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Time Series Anomaly Detection Based on Graph Convolutional Networks

Cited by 154 publications

References 19 publications

Structure-aware protein–protein interaction site prediction using deep graph convolutional network

Structure-aware protein–protein interaction site prediction using deep graph convolutional network

Message Passing Neural Networks Improve Prediction of Metabolite Authenticity

SimGRL: a simple self-supervised graph representation learning framework via triplets

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