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25 papers · ranked by Valyu relevance

01
AarXiv2020
Computing Graph Neural Networks: A Survey from Algorithms to Accelerators
Sergi Abadal, Akshay Jain, Robert Guirado, Jorge López-Alonso + 1 more
'Eduard Alarcón'] Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their capability to model and learn from graph-structured data. Such an ability has strong implications in a wide variety of fields whose data is inherently relational, for which conventional neural…
97relevance
02
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…
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
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
05
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…
97relevance
06
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…
95relevance
07
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…
94relevance
08
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…
94relevance
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…
94relevance
10
BbioRxiv17 May 2020
BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis
Xiaoxiao Li, Yuan Zhou, Siyuan Gao, Nicha Dvornek + 7 more
Understanding how certain brain regions relate to a specific neurological disorder or cognitive stimuli has been an important area of neuroimaging research. We propose BrainGNN, a graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI) and discover neurological biomarkers. In…
94relevance
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…
93relevance
12
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…
93relevance
13
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…
93relevance
14
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…
93relevance
15
AarXiv2018
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
Christopher G. Morris, Martin Ritzert, Matthias Fey, William L. Hamilton + 3 more
'William L. Hamilton' 'Jan Eric Lenssen' 'Gaurav Rattan' 'Martin Grohe'] In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion. Up to now, GNNs have only been evaluated empirically—showing…
92relevance
16
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
17
BbioRxiv5 Nov 2020
Estimating latent positions of actors using Neural Networks in R with GCN4R
Joshua Levy, Carly Bobak, Brock Christensen, Louis Vaickus + 1 more
Network analysis methods are useful to better understand and contextualize relationships between entities. While statistical and machine learning prediction models generally assume independence between actors, network-based statistical methods for social network data allow for dyadic dependence between actors. While…
89relevance
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…
88relevance
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…
87relevance
20
PPubMed10 Nov 2020
Biological network analysis with deep learning
Giulia Muzio, Leslie O’Bray, Karsten Borgwardt
Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in…
87relevance
21
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…
86relevance
22
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
23
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…
85relevance
24
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
25
BbioRxiv22 Dec 2020
CellVGAE: An unsupervised scRNA-seq analysis workflow with graph attention networks
David Buterez, Ioana Bica, Ifrah Tariq, Helena Andrés-Terré + 1 more
Currently, single-cell RNA sequencing (scRNA-seq) allows high-resolution views of individual cells, for libraries of up to (tens of) thousands of samples. In this study, we introduce the use of graph neural networks (GNN) in the unsupervised study of scRNA-seq data, namely for dimensionality reduction and clustering.…
82relevance

Results for graph neural network

Computing Graph Neural Networks: A Survey from Algorithms to Accelerators

Graph Neural Networks and Their Current Applications in Bioinformatics

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

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

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

LGNN: a novel linear graph neural network algorithm

Graph Mixer Networks

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

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

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis

GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks

Graph-level Neural Networks: Current Progress and Future Directions

Learning Graph Representations

Graph Adaptive Attention Network with Cross-Entropy

Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks

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

Estimating latent positions of actors using Neural Networks in R with GCN4R

Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty

LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction

Biological network analysis with deep learning

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

Residual Gated Graph ConvNets

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

Neural Scaling of Deep Chemical Models

CellVGAE: An unsupervised scRNA-seq analysis workflow with graph attention networks

Computing Graph Neural Networks: A Survey from Algorithms to Accelerators

Graph Neural Networks and Their Current Applications in Bioinformatics

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

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

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

LGNN: a novel linear graph neural network algorithm

Graph Mixer Networks

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

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

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis

GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks

Graph-level Neural Networks: Current Progress and Future Directions

Learning Graph Representations

Graph Adaptive Attention Network with Cross-Entropy

Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks

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

Estimating latent positions of actors using Neural Networks in R with GCN4R

Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty

LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction

Biological network analysis with deep learning

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

Residual Gated Graph ConvNets

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

Neural Scaling of Deep Chemical Models

CellVGAE: An unsupervised scRNA-seq analysis workflow with graph attention networks