Search · four archives

Results for Graph Neural Networks

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

28 papers · ranked by Valyu relevance

01
AarXiv2022
Graph Representation Learning for Wireless Communications
Maryam Mohsenivatani, Samad Ali, Vismika Ranasinghe, Nandana Rajatheva + 1 more
'Nandana Rajatheva' 'Matti Latva‐aho'] Abstract—Wireless networks are inherently graph-structured, which can be utilized in graph representation learning to solve complex wireless network optimization problems. In graph representation learning, feature vectors for each entity in the network are calculated such that…
97relevance
02
AarXiv2022
Graph-level Neural Networks: Current Progress and Future Directions
Ge Zhang, Jia Wu, Jian Yang, Shan Xue + 5 more
'Hao Peng' 'Quan Z. Sheng' 'Charų C. Aggarwal'] Graph-structured data consisting of objects (i.e., nodes) and relationships among objects (i.e., edges) are ubiquitous. Graph-level learning is a matter of studying a collection of graphs instead of a single graph. Traditional graph-level learning methods used to be the…
97relevance
03
PPubMed19 Oct 2021
Graph representation learning for structural proteomics
Romanos Fasoulis, Georgios Paliouras, Lydia E. Kavraki
The field of structural proteomics, which is focused on studying the structure-function relationship of proteins and protein complexes, is experiencing rapid growth. Since the early 2000s, structural databases such as the Protein Data Bank are storing increasing amounts of protein structural data, in addition to…
97relevance
04
PPubMed20 Jan 2022
Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs Alves et al. GNN Applied in Drug Discovery
Luiz Anastacio Alves, Natiele Carla da Silva Ferreira, Victor Maricato, Anael Viana Pinto Alberto + 2 more
'Victor Maricato' 'Anael Viana Pinto Alberto' 'Evellyn Araujo Dias' 'Nt Jose Aguiar Coelho'] Despite the increasing number of pharmaceutical companies, university laboratories and funding, less than one percent of initially researched drugs enter the commercial market. In this context, virtual screening (VS) has gained…
97relevance
05
PPubMed11 May 2022
DeepNC: a framework for drug-target interaction prediction with graph neural networks
Huu Ngoc Tran Tran, J. Joshua Thomas, Nurul Hashimah Ahamed Hassain Malim, Yuriy Orlov
'Nurul Hashimah Ahamed Hassain Malim' 'Yuriy Orlov'] The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational models, notably in the deep learning approach, scientists have been able to shorten the time spent on this stage.…
97relevance
06
BbioRxiv12 Jan 2022
GNN-SubNet: disease subnetwork detection with explainable Graph Neural Networks
Bastian Pfeifer, Afan Secic, Anna Saranti, Andreas Holzinger
The tremendous success of graphical neural networks (GNNs) has already had a major impact on systems biology research. For example, GNNs are currently used for drug target recognition in protein-drug interaction networks as well as cancer gene discovery and more. Important aspects whose practical relevance is often…
97relevance
07
BbioRxiv21 Oct 2022
Performance Evaluation of Prediction on Molecular Graphs with Graph Neural Networks
Haotian Li
Machine learning and deep learning are novel and trending approaches to solving real-world scientific problems. Graph machine learning is dedicated to performing learning methods, such as graph neural networks, on non-Euclidean data such as graphs. Molecules, with their natural graph structures, could be analyzed by…
94relevance
08
AarXiv14 Sep 2025
Quantum Graph Attention Networks: Trainable Quantum Encoders for Inductive Graph Learning
Arthur M. Faria, Mehdi Djellabi, Igor O. Sokolov, Savvas Varsamopoulos
We introduce Quantum Graph Attention Networks (QGATs) as trainable quantum encoders for inductive learning on graphs, extending the Quantum Graph Neural Networks (QGNN) framework introduced in [[1]]. QGATs leverage parameterized quantum circuits to encode node features and neighborhood structures, with quantum…
93relevance
09
AarXiv2023
Graph Mixer Networks
Ahmet Sarıgün
In recent years, the attention mechanism has demonstrated superior performance in various tasks, leading to the emergence of GAT and Graph Transformer models that utilize this mechanism to extract relational information from graph-structured data. However, the high computational cost associated with the Transformer…
92relevance
10
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…
91relevance
11
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…
90relevance
12
CchemRxiv8 Jul 2025
Efficient learning of molecular properties using Graph Neural Networks enhanced with chemistry knowledge
Authors not listed
Graph Neural Networks (GNNs) have emerged as a powerful tool in predicting molecular properties based on structural data. While GNNs excel in identifying local patterns within molecules, their ability to capture global properties remains limited due to inherent structural challenges such as oversmoothing and their…
90relevance
13
PPubMed4 Jul 2024
Graph Adaptive Attention Network with Cross-Entropy
Zhao Chen, Danilo Comminiello, Tokunbo Ogunfunmi, Nithin V. George
Non-Euclidean data, such as social networks and citation relationships between documents, have node and structural information. The Graph Convolutional Network (GCN) can automatically learn node features and association information between nodes. The core ideology of the Graph Convolutional Network is to aggregate node…
89relevance
14
BbioRxiv16 Nov 2023
MP-GNN: Graph Neural Networks to Identify Moonlighting Proteins
Hongliang Zhou, Rik Sarkar
Moonlighting proteins are those proteins that perform more than one distinct function in the body. They are pivotal in various metabolic pathways and disease mechanisms. Identifying moonlighting proteins remains a challenge in Computational Biology. In this work, we propose the first graph neural network based models…
88relevance
15
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…
87relevance
16
PPubMed10 Jul 2023
The importance of graph databases and graph learning for clinical applications
Daniel Walke, Daniel Micheel, Kay Schallert, Thilo Muth + 3 more
'David Broneske' 'Gunter Saake' 'Robert Heyer'] Title: Abstract The increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving…
87relevance
17
BbioRxiv2 Jul 2023
Inferring Neuron-level Brain Circuit Connection via Graph Neural Network Amidst Small Established Connections
Guojia Wan, Minghui Liao, Dong Zhao, Zengmao Wang + 2 more
Reconstructing neuron-level brain circuit network is a universally recognized formidable task. A significant impediment involves discerning the intricate interconnections among multitudinous neurons in a complex brain network. However, the majority of current methodologies only rely on learning local visual synapse…
86relevance
18
PPubMed14 Jun 2024
Composite Graph Neural Networks for Molecular Property Prediction
Pietro Bongini, Niccolò Pancino, Asma Bendjeddou, Franco Scarselli + 4 more
'Marco Maggini' 'Monica Bianchini' 'Amarda Shehu' 'Dong-Jun Yu'] Graph Neural Networks have proven to be very valuable models for the solution of a wide variety of problems on molecular graphs, as well as in many other research fields involving graph-structured data. Molecules are heterogeneous graphs composed of atoms…
86relevance
19
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…
85relevance
20
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…
85relevance
21
CchemRxiv16 May 2022
Neural Scaling of Deep Chemical Models
Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi + 3 more
Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of…
85relevance
22
CchemRxiv6 Mar 2025
Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data
Authors not listed
Today, machine learning models are employed extensively to predict the physicochemical and biological properties of molecules. Their performance is typically evaluated on in-distribution (ID) data, i.e., data originating from the same distribution as the training data. However, the real-world applications of such…
84relevance
23
AarXiv5 Feb 2024
Graph Neural Machine: A New Model for Learning with Tabular Data
Giannis Nikolentzos, Siyun Wang, Johannes F. Lutzeyer, Michalis Vazirgiannis
'Michalis Vazirgiannis'] In recent years, there has been a growing interest in mapping data from different domains to graph structures. Among others, neural network models such as the multi-layer perceptron (MLP) can be modeled as graphs. In fact, MLPs can be represented as directed acyclic graphs. Graph neural…
83relevance
24
PPubMed9 Sep 2024
Sub-sampling graph neural networks for genomic prediction of quantitative phenotypes
Ragini Kihlman, Ilkka Launonen, Mikko J Sillanpää, Patrik Waldmann + 1 more
'D-J de Koning'] Title: Abstract In genomics, use of deep learning (DL) is rapidly growing and DL has successfully demonstrated its ability to uncover complex relationships in large biological and biomedical data sets. With the development of high-throughput sequencing techniques, genomic markers can now be allocated…
83relevance
25
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…
83relevance
26
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…
79relevance
27
BbioRxiv28 Nov 2025
GRNITE: Gene Regulatory Network Inference with Text Embeddings
Ali Azizpour, Narein Rao, Santiago Segarra, Luay Nakhleh + 1 more
Gene regulatory networks (GRNs) capture complex regulatory relationships that govern gene expression in cells. Inference of GRNs from single-cell RNA-seq (scRNA-seq) data has been an active topic of research in the past several years. However, despite the improvements in the data quality, the GRN inference problem…
73relevance
28
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…
73relevance

Results for Graph Neural Networks

Graph Representation Learning for Wireless Communications

Graph-level Neural Networks: Current Progress and Future Directions

Graph representation learning for structural proteomics

Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs Alves et al. GNN Applied in Drug Discovery

DeepNC: a framework for drug-target interaction prediction with graph neural networks

GNN-SubNet: disease subnetwork detection with explainable Graph Neural Networks

Performance Evaluation of Prediction on Molecular Graphs with Graph Neural Networks

Quantum Graph Attention Networks: Trainable Quantum Encoders for Inductive Graph Learning

Graph Mixer Networks

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

GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks

Efficient learning of molecular properties using Graph Neural Networks enhanced with chemistry knowledge

Graph Adaptive Attention Network with Cross-Entropy

MP-GNN: Graph Neural Networks to Identify Moonlighting Proteins

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

The importance of graph databases and graph learning for clinical applications

Inferring Neuron-level Brain Circuit Connection via Graph Neural Network Amidst Small Established Connections

Composite Graph Neural Networks for Molecular Property Prediction

LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction

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

Neural Scaling of Deep Chemical Models

Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data

Graph Neural Machine: A New Model for Learning with Tabular Data

Sub-sampling graph neural networks for genomic prediction of quantitative phenotypes

Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty

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

GRNITE: Gene Regulatory Network Inference with Text Embeddings

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

Graph Representation Learning for Wireless Communications

Graph-level Neural Networks: Current Progress and Future Directions

Graph representation learning for structural proteomics

Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs Alves et al. GNN Applied in Drug Discovery

DeepNC: a framework for drug-target interaction prediction with graph neural networks

GNN-SubNet: disease subnetwork detection with explainable Graph Neural Networks

Performance Evaluation of Prediction on Molecular Graphs with Graph Neural Networks

Quantum Graph Attention Networks: Trainable Quantum Encoders for Inductive Graph Learning

Graph Mixer Networks

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

GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks

Efficient learning of molecular properties using Graph Neural Networks enhanced with chemistry knowledge

Graph Adaptive Attention Network with Cross-Entropy

MP-GNN: Graph Neural Networks to Identify Moonlighting Proteins

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

The importance of graph databases and graph learning for clinical applications

Inferring Neuron-level Brain Circuit Connection via Graph Neural Network Amidst Small Established Connections

Composite Graph Neural Networks for Molecular Property Prediction

LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction

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

Neural Scaling of Deep Chemical Models

Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data

Graph Neural Machine: A New Model for Learning with Tabular Data

Sub-sampling graph neural networks for genomic prediction of quantitative phenotypes

Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty

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

GRNITE: Gene Regulatory Network Inference with Text Embeddings

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

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