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29 papers · ranked by Valyu relevance

01
AarXiv2018
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang + 5 more
'Zhiyuan Liu' 'Lifeng Wang' 'Changcheng Li' 'Maosong Sun'] Abstract—Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics system, learning molecular fingerprints, predicting protein interface, and classifying diseases require a model to learn…
97relevance
02
AarXiv16 Dec 2020
Graph Neural Networks: Taxonomy, Advances and Trends.
Yu Zhou, Haixia Zheng, Xin Huang
YU ZHOU∗ , College of Data Science/Shanxi Spatial Information Network Engineering Technology Research Center, Taiyuan University of Technology, China HAIXIA ZHENG† , College of Data Science/Shanxi Spatial Information Network Engineering Technology Research Center, Taiyuan University of Technology, China XIN HUANG…
97relevance
03
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
04
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
05
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
06
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…
96relevance
07
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…
96relevance
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…
95relevance
09
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…
95relevance
10
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…
94relevance
11
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
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…
94relevance
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…
94relevance
14
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…
93relevance
15
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…
93relevance
16
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…
92relevance
17
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…
91relevance
18
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…
91relevance
19
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…
90relevance
20
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…
89relevance
21
PPubMed28 May 2021
Deep learning of material transport in complex neurite networks
Angran Li, Amir Barati Farimani, Yongjie Jessica Zhang
Neurons exhibit complex geometry in their branched networks of neurites which is essential to the function of individual neuron but also brings challenges to transport a wide variety of essential materials throughout their neurite networks for their survival and function. While numerical methods like isogeometric…
89relevance
22
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…
86relevance
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…
86relevance
24
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…
86relevance
25
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…
86relevance
26
BbioRxiv16 Apr 2019
Structural Learning of Proteins Using Graph Convolutional Neural Networks
Rafael Zamora-Resendiz, Silvia Crivelli
The exponential growth of protein structure databases has motivated the development of efficient deep learning methods that perform structural analysis tasks at large scale, ranging from the classification of experimentally determined proteins to the quality assessment and ranking of computationally generated protein…
84relevance
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…
75relevance
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…
75relevance
29
CchemRxiv15 Dec 2025
graphpancake: A Python package for representing organic molecules as molecular graphs utilizing electronic structure theory
Authors not listed
Computational methods for predictive modeling have been increasingly utilized in the early stages of drug discovery to supplement high-throughput screening. The advent of highly efficient and complex machine learning architectures necessitates new methods of collating the plethora of topological, geometrical, and…
71relevance

Results for Graph Neural Networks

Graph Neural Networks: A Review of Methods and Applications

Graph Neural Networks: Taxonomy, Advances and Trends.

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

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

Graph Mixer Networks

Graph Neural Networks for Node-Level Predictions

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

Graph-level Neural Networks: Current Progress and Future Directions

Learning Graph Representations

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis

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

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

MP-GNN: Graph Neural Networks to Identify Moonlighting Proteins

The importance of graph databases and graph learning for clinical applications

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

Biological network analysis with deep learning

LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction

Deep learning of material transport in complex neurite networks

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

Neural Scaling of Deep Chemical Models

Structural Learning of Proteins Using Graph Convolutional Neural Networks

GRNITE: Gene Regulatory Network Inference with Text Embeddings

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

graphpancake: A Python package for representing organic molecules as molecular graphs utilizing electronic structure theory

Graph Neural Networks: A Review of Methods and Applications

Graph Neural Networks: Taxonomy, Advances and Trends.

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

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

Graph Mixer Networks

Graph Neural Networks for Node-Level Predictions

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

Graph-level Neural Networks: Current Progress and Future Directions

Learning Graph Representations

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis

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

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

MP-GNN: Graph Neural Networks to Identify Moonlighting Proteins

The importance of graph databases and graph learning for clinical applications

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

Biological network analysis with deep learning

LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction

Deep learning of material transport in complex neurite networks

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

Neural Scaling of Deep Chemical Models

Structural Learning of Proteins Using Graph Convolutional Neural Networks

GRNITE: Gene Regulatory Network Inference with Text Embeddings

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

graphpancake: A Python package for representing organic molecules as molecular graphs utilizing electronic structure theory