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Results for Graph Classification

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14 papers · ranked by Valyu relevance

01
BbioRxiv7 Dec 2021
Graphsite: Ligand-binding site classification using Deep Graph Neural Network
Wentao Shi, Manali Singha, Limeng Pu, J. Ramanujam + 1 more
Binding sites are concave surfaces on proteins that bind to small molecules called ligands. Types of molecules that bind to the protein determine its biological function. Meanwhile, the binding process between small molecules and the protein is also crucial to various biological functionalities. Therefore, identifying…
95relevance
02
BbioRxiv10 Sep 2025
A novel Vector-Symbolic Architecture for graph encoding and its application to viral pangenome-based species classification
Fabio Cumbo, Kabir Dhillon, Jayadev Joshi, Davide Chicco + 2 more
Viral species classification is crucial for understanding viral evolution, epidemiology, and developing effective diagnostics and treatments. Traditional methods often rely on sequence similarity, which can be challenging for rapidly evolving viruses. Pangenomes, offering a comprehensive representation of species’…
80relevance
03
BbioRxiv5 May 2024
Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification
Harshini Gangapuram, Vidya Manian
Brain responses related to working memory originate from distinct brain areas and oscillate at different frequencies. EEG signals with high temporal correlation can effectively capture these responses. Therefore, estimating the functional connectivity of EEG for working memory protocols in different frequency bands…
79relevance
04
BbioRxiv13 Aug 2022
Protein Fold Classification using Graph Neural Network and Protein Topology Graph
Suri Dipannita Sayeed, Jan Niclas Wolf, Ina Koch, Guang Song
Protein fold classification reveals key structural information about proteins that is essential for understanding their function. While numerous approaches exist in the literature that classifies protein fold from sequence data using machine learning, there is hardly any approach that classifies protein fold from the…
79relevance
05
BbioRxiv29 Dec 2024
Causality-aware graph neural networks for functional stratification and phenotype prediction at scale
Charalampos P. Triantafyllidis, Ricardo Aguas
We employ a computational framework that integrates mathematical programming and graph neural networks to elucidate functional phenotypic heterogeneity in disease by classifying entire pathways under various conditions of interest. Our approach combines two distinct, yet seamlessly integrated, modeling schemes. First…
78relevance
06
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…
77relevance
07
BbioRxiv24 Nov 2022
Node-degree aware edge sampling mitigates inflated classification performance in biomedical graph representation learning
Luca Cappelletti, Lauren Rekerle, Tommaso Fontana, Peter Hansen + 13 more
Graph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many…
72relevance
08
BbioRxiv1 Jul 2022
netANOVA: novel graph clustering technique with significance assessment via hierarchical ANOVA
Diane Duroux, Kristel Van Steen
Many problems in life sciences can be brought back to a comparison of graphs. Even though a multitude of such techniques exist, often, these assume prior knowledge about the partitioning or the number of clusters and fail to provide statistical significance of observed between-network heterogeneity. Addressing these…
69relevance
09
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…
69relevance
10
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…
66relevance
11
BbioRxiv25 Sep 2024
A Graph-Informed Modeling Framework Empowering Gene Pathway Discovery
Yihao Wang, Yue Wang, Jin Jin
This study introduces a novel graph-informed modeling framework for improving the statistical analysis of gene expression data, particularly in the context of identifying differentially expressed gene pathways and gene expression-assisted disease classification in a high-dimensional data setting. By integrating gene…
65relevance
12
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…
65relevance
13
BbioRxiv18 Oct 2022
WGT: Tools and algorithms for recognizing, visualizing and generating Wheeler graphs
Kuan-Hao Chao, Pei-Wei Chen, Sanjit A. Seshia, Ben Langmead
A Wheeler graph represents a collection of strings in a way that is particularly easy to index and query. Such a graph is a practical choice for representing a graph-shaped pangenome, and it is the foundation for current graph-based pangenome indexes. However, there are no practical tools to visualize or to check…
64relevance
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…
61relevance

Results for Graph Classification

Graphsite: Ligand-binding site classification using Deep Graph Neural Network

A novel Vector-Symbolic Architecture for graph encoding and its application to viral pangenome-based species classification

Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification

Protein Fold Classification using Graph Neural Network and Protein Topology Graph

Causality-aware graph neural networks for functional stratification and phenotype prediction at scale

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

Node-degree aware edge sampling mitigates inflated classification performance in biomedical graph representation learning

netANOVA: novel graph clustering technique with significance assessment via hierarchical ANOVA

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

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

A Graph-Informed Modeling Framework Empowering Gene Pathway Discovery

MP-GNN: Graph Neural Networks to Identify Moonlighting Proteins

WGT: Tools and algorithms for recognizing, visualizing and generating Wheeler graphs

treecompareR: Tree Visualizations of Chemical Space

Graphsite: Ligand-binding site classification using Deep Graph Neural Network

A novel Vector-Symbolic Architecture for graph encoding and its application to viral pangenome-based species classification

Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification

Protein Fold Classification using Graph Neural Network and Protein Topology Graph

Causality-aware graph neural networks for functional stratification and phenotype prediction at scale

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

Node-degree aware edge sampling mitigates inflated classification performance in biomedical graph representation learning

netANOVA: novel graph clustering technique with significance assessment via hierarchical ANOVA

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

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

A Graph-Informed Modeling Framework Empowering Gene Pathway Discovery

MP-GNN: Graph Neural Networks to Identify Moonlighting Proteins

WGT: Tools and algorithms for recognizing, visualizing and generating Wheeler graphs

treecompareR: Tree Visualizations of Chemical Space