Search · four archives

Results for graph convolutional mapping

or ask it as a questionAsk the stacks →

ArchivesarXiv PubMed bioRxiv chemRxiv

WhenAny time Past month Past year Past 5 years

OrderMost relevant Most cited Newest

26 papers · ranked by Valyu relevance

01
AarXiv2022
SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Data
David Hart, Michael Whitney, Bryan S. Morse
Convolutional Neural Networks have revolutionized vision applications. There are image domains and representations, however, that cannot be handled by standard CNNs (e.g., spherical images, superpixels). Such data are usually processed using networks and algorithms specialized for each type. In this work, we show that…
85relevance
02
BbioRxiv14 Oct 2021
Deep learning models of cognitive processes constrained by human brain connectomes
Yu Zhang, Nicolas Farrugia, Pierre Bellec
Brain decoding aims to infer cognitive states from recordings of brain activity. Current literature has mainly focused on isolated brain regions engaged in specific experimental conditions, but ignored the integrative nature of cognitive processes recruiting distributed brain networks. To tackle this issue, we propose…
83relevance
03
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…
83relevance
04
AarXiv2022
Learning Graph Structure from Convolutional Mixtures
Max Wasserman, Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro
Machine learning frameworks such as graph neural networks typically rely on a given, fixed graph to exploit relational inductive biases and thus effectively learn from network data. However, when said graphs are (partially) unobserved, noisy, or dynamic, the problem of inferring graph structure from data becomes…
80relevance
05
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…
79relevance
06
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…
78relevance
07
PPubMed21 Apr 2023
Graph Representation Learning and Its Applications: A Survey
Van Thuy Hoang, Hyeon-Ju Jeon, Eun-Soon You, Yoewon Yoon + 3 more
Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. Graph representation learning aims to map graph entities to low-dimensional…
77relevance
08
AarXiv22 Mar 2024
GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks
Sukhdeep Singh, Anuj Sharma, Vinod Kumar Chauhan
Graph Neural Networks (GNN) has emerged as a popular and standard approach for learning from graph-structured data. The literature on GNN highlights the potential of this evolving research area and its widespread adoption in reallife applications. However, most of the approaches are either new in concept or derived…
77relevance
09
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…
77relevance
10
AarXiv2022
From Spectral Graph Convolutions to Large Scale Graph Convolutional Networks
Matteo Bunino
Graph Convolutional Networks (GCNs) have been shown to be a powerful concept that has been successfully applied to a large variety of tasks across many domains over the past years. In this work we study the theory that paved the way to the definition of GCN, including related parts of classical graph theory. We also…
76relevance
11
AarXiv5 Sep 2023
Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis
Mohammad Sabbaqi, Elvin Isufi
— Devising and analysing learning models for spatiotemporal network data is of importance for tasks including forecasting, anomaly detection, and multi-agent coordination, among others. Graph Convolutional Neural Networks (GCNNs) are an established approach to learn from time-invariant network data. The graph…
76relevance
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…
75relevance
13
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…
74relevance
14
PPubMed29 Nov 2022
Graph Multihead Attention Pooling with Self-Supervised Learning
Yu Wang, Liang Hu, Yang Wu, Wanfu Gao + 1 more
Graph neural networks (GNNs), which work with graph-structured data, have attracted considerable attention and achieved promising performance on graph-related tasks. While the majority of existing GNN methods focus on the convolutional operation for encoding the node representations, the graph pooling operation, which…
71relevance
15
AarXiv15 Apr 2024
Neighbour-level Message Interaction Encoding for Improved Representation Learning on Graphs
Haimin Zhang, Min Xu
Learning on Graphs Authors: ['Haimin Zhang' 'Min Xu'] Abstract—Message passing has become the dominant framework in graph representation learning. The essential idea of the message-passing framework is to update node embeddings based on the information aggregated from local neighbours. However, most existing…
71relevance
16
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…
70relevance
17
AarXiv2023
A Systematic Review of Deep Graph Neural Networks: Challenges, Classification, Architectures, Applications & Potential Utility in Bioinformatics
Adil Mudasir Malla, Asif Ali Banka
— In recent years, tasks of machine learning ranging from image processing & audio/video analysis to natural language understanding have been transformed by deep learning. The data content in all these scenarios are expressed via Euclidean space. However, a considerable amount of application data is structured in…
69relevance
18
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…
69relevance
19
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…
67relevance
20
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…
67relevance
21
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…
65relevance
22
PPubMed22 Jan 2025
Ensemble graph auto-encoders for clustering and link prediction
Chengxin Xie, Jingui Huang, Yongjiang Shi, Hui Pang + 3 more
'Xiumei Wen' 'Marco Piangerelli'] Graph auto-encoders are a crucial research area within graph neural networks, commonly employed for generating graph embeddings while minimizing errors in unsupervised learning. Traditional graph auto-encoders focus on reconstructing minimal graph data loss to encode neighborhood…
64relevance
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…
63relevance
24
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…
59relevance
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…
59relevance
26
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…
54relevance

Results for graph convolutional mapping

SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Data

Deep learning models of cognitive processes constrained by human brain connectomes

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

Learning Graph Structure from Convolutional Mixtures

Dual-channel deep graph convolutional neural networks

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

Graph Representation Learning and Its Applications: A Survey

GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks

Benchmarking Geometric Deep Learning for Cortical Segmentation and Neurodevelopmental Phenotype Prediction

From Spectral Graph Convolutions to Large Scale Graph Convolutional Networks

Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis

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

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

Graph Multihead Attention Pooling with Self-Supervised Learning

Neighbour-level Message Interaction Encoding for Improved Representation Learning on Graphs

Flippable Siamese Differential Neural Network for Differential Graph Inference

A Systematic Review of Deep Graph Neural Networks: Challenges, Classification, Architectures, Applications &amp; Potential Utility in Bioinformatics

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

Ensemble graph auto-encoders for clustering and link prediction

Predicting Molecular Trajectories using Machine Learning Methods

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

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

Application of vision transformers to protein-ligand affinity prediction.

SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Data

Deep learning models of cognitive processes constrained by human brain connectomes

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

Learning Graph Structure from Convolutional Mixtures

Dual-channel deep graph convolutional neural networks

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

Graph Representation Learning and Its Applications: A Survey

GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks

Benchmarking Geometric Deep Learning for Cortical Segmentation and Neurodevelopmental Phenotype Prediction

From Spectral Graph Convolutions to Large Scale Graph Convolutional Networks

Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis

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

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

Graph Multihead Attention Pooling with Self-Supervised Learning

Neighbour-level Message Interaction Encoding for Improved Representation Learning on Graphs

Flippable Siamese Differential Neural Network for Differential Graph Inference

A Systematic Review of Deep Graph Neural Networks: Challenges, Classification, Architectures, Applications &amp; Potential Utility in Bioinformatics

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

Ensemble graph auto-encoders for clustering and link prediction

Predicting Molecular Trajectories using Machine Learning Methods

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

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

Application of vision transformers to protein-ligand affinity prediction.

A Systematic Review of Deep Graph Neural Networks: Challenges, Classification, Architectures, Applications & Potential Utility in Bioinformatics

A Systematic Review of Deep Graph Neural Networks: Challenges, Classification, Architectures, Applications & Potential Utility in Bioinformatics