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Results for Graph Classification

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14 papers · ranked by Valyu relevance

01
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…
90relevance
02
PPubMed4 Feb 2021
Survey on graph embeddings and their applications to machine learning problems on graphs
Ilya Makarov, Dmitrii Kiselev, Nikita Nikitinsky, Lovro Subelj + 1 more
Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of…
82relevance
03
PPubMed9 Mar 2023
A Robust Automated Analog Circuits Classification Involving a Graph Neural Network and a Novel Data Augmentation Strategy
Ali Deeb, Abdalrahman Ibrahim, Mohamed Salem, Joachim Pichler + 6 more
'Sergii Tkachov' 'Anjeza Karaj' 'Fadi Al Machot' 'Kyamakya Kyandoghere' 'Marco Lanuzza' 'Lionel Trojman'] Analog mixed-signal (AMS) verification is one of the essential tasks in the development process of modern systems-on-chip (SoC). Most parts of the AMS verification flow are already automated, except for stimuli…
80relevance
04
BbioRxiv15 Sep 2024
Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks
Lida Kanari, Stanislav Schmidt, Francesco Casalegno, Emilie Delattre + 7 more
The shape of neuronal morphologies plays a critical role in determining their dynamical properties and the functionality of the brain. With an abundance of neuronal morphology reconstructions, a robust definition of cell types is important to understand their role in brain functionality. However, an objective…
80relevance
05
PPubMed22 Aug 2023
Learning and reasoning with graph data
Manfred Jaeger
Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks. Graphs are also the underlying structures of interest in a wide range of more traditional fields…
77relevance
06
PPubMed14 Oct 2011
Graph Constrained Discriminant Analysis: A New Method for the Integration of a Graph into a Classification Process Graph Integration for Microarray Data Analysis
Vincent Guillemot, Arthur Tenenhaus, Laurent Le Brusquet, Vincent Frouin + 1 more
'Vincent Frouin' 'Arkady B. Khodursky'] Integrating gene regulatory networks (GRNs) into the classification process of DNA microarrays is an important issue in bioinformatics, both because this information has a true biological interest and because it helps in the interpretation of the final classifier. We present a…
77relevance
07
PPubMed8 Sep 2022
Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier
Serkan Ucer, Tansel Ozyer, Reda Alhajj
We propose a new type of supervised visual machine learning classifier, GSNAc, based on graph theory and social network analysis techniques. In a previous study, we employed social network analysis techniques and introduced a novel classification model (called Social Network Analysis-based Classifier-SNAc) which…
76relevance
08
BbioRxiv19 Feb 2026
On why and how to encode probability distributions on graph representations of omics data: enhancing predictive tasks and knowledge discovery
Daniel M. Gonçalves, André Patrício, Rafael S. Costa, Rui Henriques
The growing availability and complexity of omics data have driven the development of specialized algorithms for modeling molecular systems. Although graph-based learning methods effectively represent biological interactions, they often neglect the statistical information embedded in node and edge annotations. To…
71relevance
09
BbioRxiv3 Sep 2020
Graphia: A platform for the graph-based visualisation and analysis of complex data
Tom C. Freeman, Sebastian Horsewell, Anirudh Patir, Josh Harling-Lee + 5 more
Quantitative and qualitative data derived from the analysis of genomes, genes, proteins or metabolites from tissue or cells are currently generated in huge volumes during biomedical research. Graphia is an open-source platform created for the graph-based analysis of such complex data, e.g. transcriptomics, proteomics…
69relevance
10
PPubMed9 Apr 2021
An analysis of the graph processing landscape
Miguel E. Coimbra, Alexandre P. Francisco, Luís Veiga
The value of graph-based big data can be unlocked by exploring the topology and metrics of the networks they represent, and the computational approaches to this exploration take on many forms. For the use-case of performing global computations over a graph, it is first ingested into a graph processing system from one…
69relevance
11
BbioRxiv18 Jan 2017
Superbubbles, Ultrabubbles and Cacti
Benedict Paten, Adam M Novak, Erik Garrison, Glenn Hickey
A superbubble is a type of directed acyclic subgraph with single distinct source and sink vertices. In genome assembly and genetics, the possible paths through a superbubble can be considered to represent the set of possible sequences at a location in a genome. Bidirected and biedged graphs are a generalization of…
69relevance
12
BbioRxiv5 Aug 2019
Supervised-learning is an accurate method for network-based gene classification
Renming Liu, Christopher A Mancuso, Anna Yannakopoulos, Kayla A Johnson + 1 more
Assigning every human gene to specific functions, diseases, and traits is a grand challenge in modern genetics. Key to addressing this challenge are computational methods such as supervised-learning and label-propagation that can leverage molecular interaction networks to predict gene attributes. In spite of being a…
68relevance
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…
67relevance
14
BbioRxiv10 Apr 2025
treecompareR: Tree Visualizations of Chemical Space
Paul Kruse, Caroline Ring
This paper presents the treecompareR package for R, which provides tools for reproducible visualizations of data through the use of taxonomies. The package builds on developments from ggplot2 and ggtree to provide visualizations tailored for use with taxonomic classification data. Additionally, it provides tools that…
63relevance

Results for Graph Classification

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

Survey on graph embeddings and their applications to machine learning problems on graphs

A Robust Automated Analog Circuits Classification Involving a Graph Neural Network and a Novel Data Augmentation Strategy

Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks

Learning and reasoning with graph data

Graph Constrained Discriminant Analysis: A New Method for the Integration of a Graph into a Classification Process Graph Integration for Microarray Data Analysis

Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier

On why and how to encode probability distributions on graph representations of omics data: enhancing predictive tasks and knowledge discovery

Graphia: A platform for the graph-based visualisation and analysis of complex data

An analysis of the graph processing landscape

Superbubbles, Ultrabubbles and Cacti

Supervised-learning is an accurate method for network-based gene classification

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

treecompareR: Tree Visualizations of Chemical Space

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

Survey on graph embeddings and their applications to machine learning problems on graphs

A Robust Automated Analog Circuits Classification Involving a Graph Neural Network and a Novel Data Augmentation Strategy

Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks

Learning and reasoning with graph data

Graph Constrained Discriminant Analysis: A New Method for the Integration of a Graph into a Classification Process Graph Integration for Microarray Data Analysis

Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier

On why and how to encode probability distributions on graph representations of omics data: enhancing predictive tasks and knowledge discovery

Graphia: A platform for the graph-based visualisation and analysis of complex data

An analysis of the graph processing landscape

Superbubbles, Ultrabubbles and Cacti

Supervised-learning is an accurate method for network-based gene classification

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

treecompareR: Tree Visualizations of Chemical Space