194 results match your criteria gnn

Enhancing Graph Neural Networks by a High-quality Aggregation of Beneficial Information.

Neural Netw 2021 Apr 24;142:20-33. Epub 2021 Apr 24.

School of Computer Science, National Engineering Research Center for Multimedia Software and Institute of Artificial Intelligence, Wuhan University, PR China; Shenzhen Research Institute, Wuhan University, PR China. Electronic address:

Graph Neural Networks (GNNs), such as GCN, GraphSAGE, GAT, and SGC, have achieved state-of-the-art performance on a wide range of graph-based tasks. These models all use a technique called neighborhood aggregation, in which the embedding of each node is updated by aggregating the embeddings of its neighbors. However, not all information aggregated from neighbors is beneficial. Read More

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Damage Localization and Severity Assessment of a Cable-Stayed Bridge Using a Message Passing Neural Network.

Sensors (Basel) 2021 Apr 30;21(9). Epub 2021 Apr 30.

Civil and Environmental Engineering, Sejong University, Seoul 05006, Korea.

Cable-stayed bridges are damaged by multiple factors such as natural disasters, weather, and vehicle load. In particular, if the stayed cable, which is an essential and weak component of the cable-stayed bridge, is damaged, it may adversely affect the adjacent cables and worsen the bridge structure condition. Therefore, we must accurately determine the condition of the cable with a technology-based evaluation strategy. Read More

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An effective self-supervised framework for learning expressive molecular global representations to drug discovery.

Brief Bioinform 2021 May 3. Epub 2021 May 3.

Tsinghua Laboratory of Brain and Intelligence and Department of Biomedical Engineering, Tsinghua University, Haidian, 100084 Beijing, China.

How to produce expressive molecular representations is a fundamental challenge in artificial intelligence-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for modeling molecular data. However, previous supervised approaches usually suffer from the scarcity of labeled data and poor generalization capability. Read More

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Expert recommendations based on link prediction during the COVID-19 outbreak.

Hui Wang ZiChun Le

Scientometrics 2021 Apr 26:1-20. Epub 2021 Apr 26.

College of Science, Zhejiang University of Technology, Hangzhou, 310023 PR China.

Since the emergence of COVID-19, the number of infections has significantly increased. As of April 7, 8:00 am, the total number of global infections has already reached 1,338,415, with the number of deaths being 74,556. Medical experts from various countries have conducted relevant researches in their own fields and countries, and the development of an effective vaccine has been expected soon. Read More

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Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks.

Int J Environ Res Public Health 2021 04 6;18(7). Epub 2021 Apr 6.

Department of Sports Sciences, MATIM, Université de Reims Champagne-Ardenne, 51100 Reims, France.

The COVID-19 pandemic has had unprecedented social and economic consequences in the United States. Therefore, accurately predicting the dynamics of the pandemic can be very beneficial. Two main elements required for developing reliable predictions include: (1) a predictive model and (2) an indicator of the current condition and status of the pandemic. Read More

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Deep Constraint-based Propagation in Graph Neural Networks.

IEEE Trans Pattern Anal Mach Intell 2021 Apr 15;PP. Epub 2021 Apr 15.

The popularity of deep learning techniques renewed the interest in neural architectures able to process complex structures that can be represented using graphs, inspired by Graph Neural Networks (GNNs). We focus our attention on the originally proposed GNN model of Scarselli et al. 2009, which encodes the state of the nodes of the graph by means of an iterative diffusion procedure that, during the learning stage, must be computed at every epoch, until the fixed point of a learnable state transition function is reached, propagating the information among the neighbouring nodes. Read More

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A graph neural network framework for causal inference in brain networks.

Sci Rep 2021 Apr 13;11(1):8061. Epub 2021 Apr 13.

CIML, Biophysics, University of Regensburg, 93040, Regensburg, Germany.

A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. Read More

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Density Prediction Models for Energetic Compounds Merely Using Molecular Topology.

J Chem Inf Model 2021 Apr 12. Epub 2021 Apr 12.

Institute of Chemical Materials, China Academy of Engineering Physics (CAEP), P.O. Box 919-311, Mianyang 621999, Sichuan, China.

Newly developed high-throughput methods for property predictions make the process of materials design faster and more efficient. Density is an important physical property for energetic compounds to assess detonation velocity and detonation pressure, but the time cost of recent density prediction models is still high owing to the time-consuming processes to calculate molecular descriptors. To improve the screening efficiency of potential energetic compounds, new methods for density prediction with more accuracy and less time cost are urgently needed, and a possible solution is to establish direct mappings between the molecular structure and density. Read More

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Multigraph Transformer for Free-Hand Sketch Recognition.

IEEE Trans Neural Netw Learn Syst 2021 Apr 7;PP. Epub 2021 Apr 7.

Learning meaningful representations of free-hand sketches remains a challenging task given the signal sparsity and the high-level abstraction of sketches. Existing techniques have focused on exploiting either the static nature of sketches with convolutional neural networks (CNNs) or the temporal sequential property with recurrent neural networks (RNNs). In this work, we propose a new representation of sketches as multiple sparsely connected graphs. Read More

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NPI-GNN: Predicting ncRNA-protein interactions with deep graph neural networks.

Brief Bioinform 2021 Apr 5. Epub 2021 Apr 5.

College of Intelligence and Computing, Tianjin University, Tianjin 300350, China.

Noncoding RNAs (ncRNAs) play crucial roles in many biological processes. Experimental methods for identifying ncRNA-protein interactions (NPIs) are always costly and time-consuming. Many computational approaches have been developed as alternative ways. Read More

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Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning.

IEEE Trans Neural Netw Learn Syst 2021 Apr 5;PP. Epub 2021 Apr 5.

Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Read More

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A Novel Graph Neural Network Methodology to Investigate Dihydroorotate Dehydrogenase Inhibitors in Small Cell Lung Cancer.

Biomolecules 2021 Mar 23;11(3). Epub 2021 Mar 23.

Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 510275, China.

Small cell lung cancer (SCLC) is a particularly aggressive tumor subtype, and dihydroorotate dehydrogenase (DHODH) has been demonstrated to be a therapeutic target for SCLC. Network pharmacology analysis and virtual screening were utilized to find out related proteins and investigate candidates with high docking capacity to multiple targets. Graph neural networks (GNNs) and machine learning were used to build reliable predicted models. Read More

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PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features.

PLoS One 2021 17;16(3):e0248682. Epub 2021 Mar 17.

Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (KMUTT), Bang Khun Thian, Bangkok, Thailand.

A new web server called PhotoModPlus is presented as a platform for predicting photosynthetic proteins via genome neighborhood networks (GNN) and genome neighborhood-based machine learning. GNN enables users to visualize the overview of the conserved neighboring genes from multiple photosynthetic prokaryotic genomes and provides functional guidance on the query input. In the platform, we also present a new machine learning model utilizing genome neighborhood features for predicting photosynthesis-specific functions based on 24 prokaryotic photosynthesis-related GO terms, namely PhotoModGO. Read More

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A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction.

Int J Mach Learn Cybern 2020 Jun 23:1-8. Epub 2020 Jun 23.

Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China.

Electronic health records (EHRs) have been widely used to help physicians to make decisions by predicting medical events such as diseases, prescriptions, outcomes, and so on. How to represent patient longitudinal medical data is the key to making these predictions. Recurrent neural network (RNN) is a popular model for patient longitudinal medical data representation from the view of patient status sequences, but it cannot represent complex interactions among different types of medical information, i. Read More

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Polypharmacy Side effect Prediction with Enhanced Interpretability Based on Graph Feature Attention Network.

Bioinformatics 2021 Mar 14. Epub 2021 Mar 14.

Department of Industrial Engineering, Ajou University, Wonchun-dong, Yeongtong-gu, Suwon 443-749, South Korea.

Motivation: Polypharmacy side effects should be carefully considered for new drug development. However, considering all the complex drug-drug interactions that cause polypharma-cy side effects is challenging. Recently, graph neural network (GNN) models have handled these complex interactions successfully and shown great predictive perfor-mance. Read More

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Ricci Curvature-Based Semi-Supervised Learning on an Attributed Network.

Entropy (Basel) 2021 Feb 27;23(3). Epub 2021 Feb 27.

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

In recent years, on the basis of drawing lessons from traditional neural network models, people have been paying more and more attention to the design of neural network architectures for processing graph structure data, which are called graph neural networks (GNN). GCN, namely, graph convolution networks, are neural network models in GNN. GCN extends the convolution operation from traditional data (such as images) to graph data, and it is essentially a feature extractor, which aggregates the features of neighborhood nodes into those of target nodes. Read More

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February 2021

A Barrier Varying-Parameter Dynamic Learning Network for Solving Time-Varying Quadratic Programming Problems With Multiple Constraints.

IEEE Trans Cybern 2021 Feb 26;PP. Epub 2021 Feb 26.

Many scientific research and engineering problems can be converted to time-varying quadratic programming (TVQP) problems with constraints. Thus, TVQP problem solving plays an important role in practical applications. Many existing neural networks, such as the gradient neural network (GNN) or zeroing neural network (ZNN), were designed to solve TVQP problems, but the convergent rate is limited. Read More

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February 2021

Association of SARS-CoV-2 viral load at admission with in-hospital acute kidney injury: A retrospective cohort study.

PLoS One 2021 24;16(2):e0247366. Epub 2021 Feb 24.

The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the associated Coronavirus Disease 2019 (COVID-19) is a public health emergency. Acute kidney injury (AKI) is a common complication in hospitalized patients with COVID-19 although mechanisms underlying AKI are yet unclear. There may be a direct effect of SARS-CoV-2 virus on the kidney; however, there is currently no data linking SARS-CoV-2 viral load (VL) to AKI. Read More

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Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models.

J Cheminform 2021 Feb 17;13(1):12. Epub 2021 Feb 17.

Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China.

Graph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies have shown that GNN could yield more promising results than traditional descriptor-based methods. In this study, based on 11 public datasets covering various property endpoints, the predictive capacity and computational efficiency of the prediction models developed by eight machine learning (ML) algorithms, including four descriptor-based models (SVM, XGBoost, RF and DNN) and four graph-based models (GCN, GAT, MPNN and Attentive FP), were extensively tested and compared. The results demonstrate that on average the descriptor-based models outperform the graph-based models in terms of prediction accuracy and computational efficiency. Read More

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February 2021

Auto-Metric Graph Neural Network Based on a Meta-learning Strategy for the Diagnosis of Alzheimer's disease.

IEEE J Biomed Health Inform 2021 Jan 25;PP. Epub 2021 Jan 25.

Alzheimer's disease (AD) is the most common cognitive disorder. In recent years, many computer-aided diagnosis techniques have been proposed for AD diagnosis and progression predictions. Among them, graph neural networks (GNNs) have received extensive attention owing to their ability to effectively fuse multimodal features and model the correlation between samples. Read More

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January 2021

On Inductive-Transductive Learning with Graph Neural Networks.

IEEE Trans Pattern Anal Mach Intell 2021 Jan 25;PP. Epub 2021 Jan 25.

Many realworld domains involve information naturally represented by graphs, where nodes denote basic patterns while edges stand for relationships among them. The Graph Neural Network (GNN) is a machine learning model capable of directly managing graphstructured data. In the original framework, GNNs are inductively trained, adapting their parameters based on a supervised learning environment. Read More

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January 2021

Hypergraph Neural Network for Skeleton-Based Action Recognition.

IEEE Trans Image Process 2021 26;30:2263-2275. Epub 2021 Jan 26.

Recently, skeleton-based human action recognition has attracted a lot of research attention in the field of computer vision. Graph convolutional networks (GCNs), which model the human body skeletons as spatial-temporal graphs, have shown excellent results. However, the existing methods only focus on the local physical connection between the joints, and ignore the non-physical dependencies among joints. Read More

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January 2021

Learning Aligned Image-Text Representations Using Graph Attentive Relational Network.

IEEE Trans Image Process 2021 18;30:1840-1852. Epub 2021 Jan 18.

Image-text matching aims to measure the similarities between images and textual descriptions, which has made great progress recently. The key to this cross-modal matching task is to build the latent semantic alignment between visual objects and words. Due to the widespread variations of sentence structures, it is very difficult to learn the latent semantic alignment using only global cross-modal features. Read More

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January 2021

BERT-GT: Cross-sentence n-ary relation extraction with BERT and graph transformer.

Bioinformatics 2021 Jan 8. Epub 2021 Jan 8.

National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, 20894, USA.

Motivation: A biomedical relation statement is commonly expressed in multiple sentences and consists of many concepts, including gene, disease, chemical, and mutation. To automatically extract information from biomedical literature, existing biomedical text-mining approaches typically formulate the problem as a cross-sentence n-ary relation-extraction task that detects relations among n entities across multiple sentences, and use either a graph neural network (GNN) with long short-term memory (LSTM) or an attention mechanism. Recently, Transformer has been shown to outperform LSTM on many natural language processing (NLP) tasks. Read More

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January 2021

Prediction of Liquid Chromatographic Retention Time with Graph Neural Networks to Assist in Small Molecule Identification.

Anal Chem 2021 02 7;93(4):2200-2206. Epub 2021 Jan 7.

College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.

The predicted liquid chromatographic retention times (RTs) of small molecules are not accurate enough for wide adoption in structural identification. In this study, we used the graph neural network to predict the retention time (GNN-RT) from structures of small molecules directly without the requirement of molecular descriptors. The predicted accuracy of GNN-RT was compared with random forests (RFs), Bayesian ridge regression, convolutional neural network (CNN), and a deep-learning regression model (DLM) on a METLIN small molecule retention time (SMRT) dataset. Read More

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February 2021

DeepImmuno: Deep learning-empowered prediction and generation of immunogenic peptides for T cell immunity.

bioRxiv 2020 Dec 24. Epub 2020 Dec 24.

T-cells play an essential role in the adaptive immune system by seeking out, binding and destroying foreign antigens presented on the cell surface of diseased cells. An improved understanding of T-cell immunity will greatly aid in the development of new cancer immunotherapies and vaccines for life threatening pathogens. Central to the design of such targeted therapies are computational methods to predict non-native epitopes to elicit a T cell response, however, we currently lack accurate immunogenicity inference methods. Read More

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December 2020

Hierarchical Representation Learning in Graph Neural Networks With Node Decimation Pooling.

IEEE Trans Neural Netw Learn Syst 2021 Feb 4;PP. Epub 2021 Feb 4.

In graph neural networks (GNNs), pooling operators compute local summaries of input graphs to capture their global properties, and they are fundamental for building deep GNNs that learn hierarchical representations. In this work, we propose the Node Decimation Pooling (NDP), a pooling operator for GNNs that generates coarser graphs while preserving the overall graph topology. During training, the GNN learns new node representations and fits them to a pyramid of coarsened graphs, which is computed offline in a preprocessing stage. Read More

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February 2021

Using Mobility Data to Understand and Forecast COVID19 Dynamics.

medRxiv 2020 Dec 15. Epub 2020 Dec 15.

Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID-19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. Read More

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December 2020

Graph Neural Network and Context-Aware Based User Behavior Prediction and Recommendation System Research.

Comput Intell Neurosci 2020 30;2020:8812370. Epub 2020 Nov 30.

School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250353, China.

Due to the influence of context information on user behavior, context-aware recommendation system (CARS) has attracted extensive attention in recent years. The most advanced context-aware recommendation system maps the original multi-field features into a shared hidden space and then simply connects it to a deep neural network (DNN) or other specially designed networks. However, for different areas, the ability of modeling complex interactions in a sufficiently flexible and explicit way is limited by the simple unstructured combination of feature fields. Read More

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November 2020

Multi-expert learning of adaptive legged locomotion.

Sci Robot 2020 12;5(49)

School of Informatics, University of Edinburgh, Edinburgh, UK.

Achieving versatile robot locomotion requires motor skills that can adapt to previously unseen situations. We propose a multi-expert learning architecture (MELA) that learns to generate adaptive skills from a group of representative expert skills. During training, MELA is first initialized by a distinct set of pretrained experts, each in a separate deep neural network (DNN). Read More

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December 2020