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27 papers · ranked by Valyu relevance

01
PPubMed10 Nov 2019
Graph convolutional networks: a comprehensive review
Si Zhang, Hanghang Tong, Jiejun Xu, Ross Maciejewski
Graphs naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. The unique capability of graphs enables capturing the structural relations among data, and thus allows to harvest more insights compared to analyzing data in isolation. However, it is often very…
91relevance
02
AarXiv2018
On Filter Size in Graph Convolutional Networks
Tran Van Dinh, Nicolò Navarin, Alessandro Sperduti
—Recently, many researchers have been focusing on the definition of neural networks for graphs. The basic component for many of these approaches remains the graph convolution idea proposed almost a decade ago. In this paper, we extend this basic component, following an intuition derived from the well-known…
88relevance
03
AarXiv2017
A Generalization of Convolutional Neural Networks to Graph-Structured Data
Yotam Hechtlinger, Purvasha Chakravarti, Jining Qin
This paper introduces a generalization of Convolutional Neural Networks (CNNs) from low-dimensional grid data, such as images, to graph-structured data. We propose a novel spatial convolution utilizing a random walk to uncover the relations within the input, analogous to the way the standard convolution uses the…
87relevance
04
PPubMed12 Jan 2024
Attention-based fusion of multiple graphheat networks for structural to functional brain mapping
Subba Reddy Oota, Archi Yadav, Arpita Dash, Raju S. Bapi + 1 more
'Avinash Sharma'] Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous fluctuations of the brain activity during the resting-state as captured by functional MRI (rsfMRI) contain rich non-stationary…
87relevance
05
AarXiv2018
Hierarchical Bipartite Graph Convolution Networks
Marcel Nassar
Recently, graph neural networks have been adopted in a wide variety of applications ranging from relational representations to modeling irregular data domains such as point clouds and social graphs. However, the space of graph neural network architectures remains highly fragmented impeding the development of optimized…
86relevance
06
AarXiv2017
Residual Gated Graph ConvNets
Xavier Bresson, Thomas Laurent
Graph-structured data such as social networks, functional brain networks, gene regulatory networks, communications networks have brought the interest in generalizing deep learning techniques to graph domains. In this paper, we are interested to design neural networks for graphs with variable length in order to solve…
85relevance
07
BbioRxiv13 Aug 2021
A-GHN: Attention-based Fusion of Multiple GraphHeat Networks for Structural to Functional Brain Mapping
Subba Reddy Oota, Archi Yadav, Arpita Dash, Raju S. Bapi + 1 more
Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous fluctuations of the brain activity during the restingstate as captured by functional MRI (rsfMRI) contain rich non-stationary dynamics over a…
84relevance
08
AarXiv18 Aug 2021
Learning Graph Representations
Rucha Bhalchandra Joshi, Subhankar Mishra
> Abstract. Social and information networks are gaining huge popularity recently due to their various applications. Knowledge representation through graphs in the form of nodes and edges should preserve as many characteristics of the original data as possible. Some of the interesting and useful applications on these…
84relevance
09
PPubMed4 Apr 2024
Dual-channel deep graph convolutional neural networks
Zhonglin Ye, Zhuoran Li, Gege Li, Haixing Zhao
The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks. However, current dual-channel graph convolutional neural networks are…
82relevance
10
PPubMed29 Jul 2021
Graph Neural Networks and Their Current Applications in Bioinformatics
Xiao-Meng Zhang, Li Liang, Lin Liu, Ming-Jing Tang
Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of…
82relevance
11
PPubMed30 Jun 2020
Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis
Byung-Hoon Kim, Jong Chul Ye
Graph neural networks (GNN) rely on graph operations that include neural network training for various graph related tasks. Recently, several attempts have been made to apply the GNNs to functional magnetic resonance image (fMRI) data. Despite recent progresses, a common limitation is its difficulty to explain the…
81relevance
12
PPubMed10 Mar 2023
A deep graph convolutional neural network architecture for graph classification A deep graph convolutional neural network architecture for graph classification
Yuchen Zhou, Hongtao Huo, Zhiwen Hou, Fanliang Bu + 1 more
Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to 4 layers, which greatly limits the ability of GCN models to…
78relevance
13
AarXiv2020
Graph Neural Networks for Node-Level Predictions
Christoph Heindl
The success of deep learning has revolutionized many fields of research including areas of computer vision, text and speech processing. Enormous research efforts have led to numerous methods that are capable of efficiently analyzing data, especially in the Euclidean space. However, many problems are posed in…
77relevance
14
BbioRxiv17 May 2022
Topology Adaptive Graph Convolution Network With Heterogeneous Entities For Predicting Adverse Events from Drug-Drug-Interactions
Srijani Bagchi, Anasua Sarkar, Ujjwal Maulik
In times like this, it is imperative to be cautious about the effects of drugs or vaccination doses on patients who are already suffering from other serious diseases. It’s not only the virus which can affect the body metabolisms, drugs to encounter the virus may also end up having unwanted negative effects. Therapeutic…
76relevance
15
BbioRxiv16 Nov 2020
Proximity Measures as Graph Convolution Matrices for Link Prediction in Biological Networks
Mustafa Coşkun, Mehmet Koyutürk
Link prediction is an important and well-studied problem in computational biology, with a broad range of applications including disease gene prioritization, drug-disease associations, and drug response in cancer. The general principle in link prediction is to use the topological characteristics and the attributes–if…
76relevance
16
AarXiv2017
Graph Convolution: A High-Order and Adaptive Approach
Zhenpeng Zhou, Xiaocheng Li
In this paper, we presented a novel convolutional neural network framework for graph modeling, with the introduction of two new modules specially designed for graph-structured data: the k-th order convolution operator and the adaptive filtering module. Importantly, our framework of High-order and Adaptive Graph…
75relevance
17
BbioRxiv2 Dec 2021
Benchmarking Geometric Deep Learning for Cortical Segmentation and Neurodevelopmental Phenotype Prediction
Abdulah Fawaz, Logan Z. J. Williams, Amir Alansary, Cher Bass + 12 more
The emerging field of geometric deep learning extends the application of convolutional neural networks to irregular domains such as graphs, meshes and surfaces. Several recent studies have explored the potential for using these techniques to analyse and segment the cortical surface. However, there has been no…
74relevance
18
BbioRxiv4 Sep 2025
Flippable Siamese Differential Neural Network for Differential Graph Inference
Jiacheng Leng, Jiating Yu, Ling-Yun Wu
Differential graph inference is a critical analytical technique that enables researchers to accurately identify the variables and their interactions that change under different conditions. By comparing two conditions, researchers can gain a deeper understanding of the differences between them. Currently, the mainstream…
72relevance
19
CchemRxiv6 Oct 2025
Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty
Authors not listed
Accurate prediction of redox potentials of iron (Fe) complexes, in tandem with uncertainty quantification, is essential to advance technologies related to electro-deposition and energy storage by enabling reliable modeling, guiding experimental design, and improving the efficiency of material discovery. Since…
72relevance
20
CchemRxiv25 Oct 2023
Encoding Protein-Ligand Interactions: Binding Affinity Prediction with Multigraph-based Modeling and Graph Convolutional Network
Nedra Mekni, Hosein Fooladi, Ugo Perricone, Thierry Langer
Machine learning models are employed to enhance the speed and provide novel insights in drug discovery due to their demonstrated effectiveness in predicting properties of small molecules like pKa, solubility, and binding affinity. These approaches accelerate drug discovery by helping researchers efficiently identify…
70relevance
21
CchemRxiv18 May 2022
Multi-fidelity machine learning models for improved high-throughput screening predictions
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
68relevance
22
CchemRxiv8 Aug 2024
LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction
Zachary Humphreys, Xenophon Evangelopoulos, Stavros Gerolymatos, Edward O. Pyzer-Knapp + 1 more
Graph neural networks have recently met huge success in various inference tasks including materials property prediction amongst many others. Nevertheless, having an inherently locally-based representation capacity as they do, global representation of materials' structures can only only be achieved by expanding the…
67relevance
23
CchemRxiv18 Dec 2023
Predicting Molecular Trajectories using Machine Learning Methods
Sonny Young
Predicting molecular trajectories is a cornerstone of computational chemistry, with implications for drug discovery and molecular dynamics simulations. This study presents a comprehensive analysis of various machine learning models for the prediction of aspirin molecular trajectories, as captured in a dataset of 1500…
64relevance
24
BbioRxiv30 Sep 2022
Interpretable brain decoding from sensations to cognition to action: graph neural networks reveal the representational hierarchy of human cognition
Yu Zhang, Lingzhong Fan, Tianzi Jiang, Alain Dagher + 1 more
Inter-subject modeling of cognitive processes has been a challenging task due to large individual variability in brain structure and function. Graph neural networks (GNNs) provide a potential way to project subject-specific neural responses onto a common representational space by effectively combining local and…
63relevance
25
CchemRxiv31 Oct 2023
Kartograf: An Accurate Geometry-Based Atom Mapper for Hybrid Topology Relative Free Energy Calculations
Benjamin Ries, Irfan Alibay, David W H Swenson, Hannah M Baumann + 3 more
Relative binding free energy (RBFE) calculations have emerged as a powerful tool supporting ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular the setup of such calculations. Atom mapping algorithms are an essential component in…
61relevance
26
CchemRxiv8 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Cory M. Simon, Xiaoli Fern
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a data set of 382 molecules with toxicity labels from…
58relevance
27
CchemRxiv18 Apr 2025
Application of vision transformers to protein-ligand affinity prediction.
Authors not listed
Predicting protein-ligand binding affinity from three-dimensional (3D) structural data is a central task in structure-based drug discovery, yet it remains challenging due to limited data availability, structural complexity, and the sparse nature of 3D molecular representations. In this study, we investigate the…
57relevance

Results for graph convolutional mapping

Graph convolutional networks: a comprehensive review

On Filter Size in Graph Convolutional Networks

A Generalization of Convolutional Neural Networks to Graph-Structured Data

Attention-based fusion of multiple graphheat networks for structural to functional brain mapping

Hierarchical Bipartite Graph Convolution Networks

Residual Gated Graph ConvNets

A-GHN: Attention-based Fusion of Multiple GraphHeat Networks for Structural to Functional Brain Mapping

Learning Graph Representations

Dual-channel deep graph convolutional neural networks

Graph Neural Networks and Their Current Applications in Bioinformatics

Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis

A deep graph convolutional neural network architecture for graph classification A deep graph convolutional neural network architecture for graph classification

Graph Neural Networks for Node-Level Predictions

Topology Adaptive Graph Convolution Network With Heterogeneous Entities For Predicting Adverse Events from Drug-Drug-Interactions

Proximity Measures as Graph Convolution Matrices for Link Prediction in Biological Networks

Graph Convolution: A High-Order and Adaptive Approach

Benchmarking Geometric Deep Learning for Cortical Segmentation and Neurodevelopmental Phenotype Prediction

Flippable Siamese Differential Neural Network for Differential Graph Inference

Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty

Encoding Protein-Ligand Interactions: Binding Affinity Prediction with Multigraph-based Modeling and Graph Convolutional Network

Multi-fidelity machine learning models for improved high-throughput screening predictions

LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction

Predicting Molecular Trajectories using Machine Learning Methods

Interpretable brain decoding from sensations to cognition to action: graph neural networks reveal the representational hierarchy of human cognition

Kartograf: An Accurate Geometry-Based Atom Mapper for Hybrid Topology Relative Free Energy Calculations

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Application of vision transformers to protein-ligand affinity prediction.

Graph convolutional networks: a comprehensive review

On Filter Size in Graph Convolutional Networks

A Generalization of Convolutional Neural Networks to Graph-Structured Data

Attention-based fusion of multiple graphheat networks for structural to functional brain mapping

Hierarchical Bipartite Graph Convolution Networks

Residual Gated Graph ConvNets

A-GHN: Attention-based Fusion of Multiple GraphHeat Networks for Structural to Functional Brain Mapping

Learning Graph Representations

Dual-channel deep graph convolutional neural networks

Graph Neural Networks and Their Current Applications in Bioinformatics

Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis

A deep graph convolutional neural network architecture for graph classification A deep graph convolutional neural network architecture for graph classification

Graph Neural Networks for Node-Level Predictions

Topology Adaptive Graph Convolution Network With Heterogeneous Entities For Predicting Adverse Events from Drug-Drug-Interactions

Proximity Measures as Graph Convolution Matrices for Link Prediction in Biological Networks

Graph Convolution: A High-Order and Adaptive Approach

Benchmarking Geometric Deep Learning for Cortical Segmentation and Neurodevelopmental Phenotype Prediction

Flippable Siamese Differential Neural Network for Differential Graph Inference

Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty

Encoding Protein-Ligand Interactions: Binding Affinity Prediction with Multigraph-based Modeling and Graph Convolutional Network

Multi-fidelity machine learning models for improved high-throughput screening predictions

LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction

Predicting Molecular Trajectories using Machine Learning Methods

Interpretable brain decoding from sensations to cognition to action: graph neural networks reveal the representational hierarchy of human cognition

Kartograf: An Accurate Geometry-Based Atom Mapper for Hybrid Topology Relative Free Energy Calculations

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Application of vision transformers to protein-ligand affinity prediction.