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25 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
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
03
PPubMed5 Dec 2022
A Survey on Graph Neural Networks for Microservice-Based Cloud Applications
Hoa Xuan Nguyen, Shaoshu Zhu, Mingming Liu, Uttam Ghosh + 3 more
'Sahil Verma' 'Gautam Srivastava' 'Mouhammd Alkasassbeh'] Graph neural networks (GNNs) have achieved great success in many research areas ranging from traffic to computer vision. With increased interest in cloud-native applications, GNNs are increasingly being investigated to address various challenges in microservice…
97relevance
04
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
05
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…
96relevance
06
BbioRxiv16 Aug 2024
Leveraging protein-protein interactions in phenotype prediction through graph neural networks
Riccardo Smeriglio, Joana Rosell-Mirmi, Petia Radeva, Jordi Abante
Current genotype-to-phenotype models, such as poly-genic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the…
94relevance
07
PPubMed23 Nov 2023
LGNN: a novel linear graph neural network algorithm
Shujuan Cao, Xiaoming Wang, Zhonglin Ye, Mingyuan Li + 1 more
The emergence of deep learning has not only brought great changes in the field of image recognition, but also achieved excellent node classification performance in graph neural networks. However, the existing graph neural network framework often uses methods based on spatial domain or spectral domain to capture network…
91relevance
08
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…
90relevance
09
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
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…
90relevance
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…
89relevance
12
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…
88relevance
13
AarXiv10 Jun 2025
Adapting to Heterophilic Graph Data with Structure-Guided Neighbor Discovery
Víctor M. Tenorio, Madeline Navarro, Samuel Rey, Santiago Segarra + 1 more
—Graph Neural Networks (GNNs) often struggle with heterophilic data, where connected nodes may have dissimilar labels, as they typically assume homophily and rely on local message passing. To address this, we propose creating alternative graph structures by linking nodes with similar structural attributes (e.g.…
88relevance
14
BbioRxiv2 Oct 2023
PhyGCN: Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning
Yihe Deng, Ruochi Zhang, Pan Xu, Jian Ma + 1 more
Hypergraphs are powerful tools for modeling complex interactions across various domains, including biomedicine. However, learning meaningful node representations from hypergraphs remains a challenge. Existing supervised methods often lack generalizability, thereby limiting their real-world applications. We propose a…
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…
85relevance
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…
85relevance
17
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…
84relevance
18
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…
84relevance
19
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…
83relevance
20
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…
82relevance
21
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…
81relevance
22
PPubMed9 Jun 2023
Path-enhanced graph convolutional networks for node classification without features
Qingju Jiao, Peige Zhao, Hanjin Zhang, Yahong Han + 2 more
'Toqir Rana'] Most current graph neural networks (GNNs) are designed from the view of methodology and rarely consider the inherent characters of graph. Although the inherent characters may impact the performance of GNNs, very few methods are proposed to resolve the issue. In this work, we mainly focus on improving the…
80relevance
23
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…
78relevance
24
BbioRxiv17 Nov 2022
gGN: learning to represent graph nodes as low-rank Gaussian distributions
Alejandro A. Edera, Georgina Stegmayer, Diego H. Milone
Unsupervised learning of node representations from knowledge graphs is critical for numerous downstream tasks, ranging from large-scale graph analysis to measuring semantic similarity between nodes. This study presents gGN as a novel representation that defines graph nodes as Gaussian distributions. Unlike existing…
74relevance
25
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…
72relevance

Results for graph neural network

Graph Representation Learning for Wireless Communications

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

A Survey on Graph Neural Networks for Microservice-Based Cloud Applications

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

Graph-level Neural Networks: Current Progress and Future Directions

Leveraging protein-protein interactions in phenotype prediction through graph neural networks

LGNN: a novel linear graph neural network algorithm

Graph Mixer Networks

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

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

GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks

Graph Adaptive Attention Network with Cross-Entropy

Adapting to Heterophilic Graph Data with Structure-Guided Neighbor Discovery

PhyGCN: Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning

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

Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty

LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction

Neural Scaling of Deep Chemical Models

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

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

Path-enhanced graph convolutional networks for node classification without features

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

gGN: learning to represent graph nodes as low-rank Gaussian distributions

GRNITE: Gene Regulatory Network Inference with Text Embeddings

Graph Representation Learning for Wireless Communications

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

A Survey on Graph Neural Networks for Microservice-Based Cloud Applications

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

Graph-level Neural Networks: Current Progress and Future Directions

Leveraging protein-protein interactions in phenotype prediction through graph neural networks

LGNN: a novel linear graph neural network algorithm

Graph Mixer Networks

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

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

GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks

Graph Adaptive Attention Network with Cross-Entropy

Adapting to Heterophilic Graph Data with Structure-Guided Neighbor Discovery

PhyGCN: Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning

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

Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty

LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction

Neural Scaling of Deep Chemical Models

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

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

Path-enhanced graph convolutional networks for node classification without features

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

gGN: learning to represent graph nodes as low-rank Gaussian distributions

GRNITE: Gene Regulatory Network Inference with Text Embeddings

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