graph neural network

Hybrid-hybrid correction of errors in long reads with HERO

HERO improves error correction in long reads by combining de Bruijn and overlap graphs, reducing indel and mismatch errors by 65% and 20% respectively.

A Graph Matching Approach to Tracking Neurons in Freely-Moving C. elegans

This paper introduces a graph matching method to track neurons in freely-moving C. elegans, achieving over 75% accuracy with five labeled frames.

Pasa: Leverage population pangenome graph to scaffold prokaryote genome assemblies.

Pasa improves genome scaffolding by using population pangenome graphs, enhancing accuracy and efficiency for bacterial genome assemblies.

LightMHC: A Light Model for pMHC Structure Prediction with Graph Neural Networks

LightMHC predicts full-atom pMHC structures from sequences using a lightweight model with attention mechanisms, achieving comparable performance to AlphaFold 2 and ESMFold with faster speed.

LightRoseTTA: High-efficient and Accurate Protein Structure Prediction Using an Ultra-Lightweight Deep Graph Model

LightRoseTTA provides a high-efficient and accurate protein structure prediction method, outperforming RoseTTAFold in speed and parameter count while maintaining competitive accuracy.

Transformer with Convolution and Graph-Node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image

This paper introduces TCGN, a deep learning model that integrates CNN, GNN, and transformer for gene expression prediction from histopathological images, achieving higher accuracy and interpretability.

Insights into Partial Folding State of Bovine Pancreatic Trypsin Inhibitor: A Combined Molecular Dynamics Simulations, Information Theory and Molecular Graph Theory Study

MP-GNN: Graph Neural Networks to Identify MoonlightingProteins

The paper introduces graph neural network models to identify moonlighting proteins, achieving high accuracy on human data and offering a lightweight approach for larger datasets.

CGCom: a framework for inferring Cell-cell Communication based on Graph Neural Network

CGCom uses a graph neural network to infer cell-cell communication from spatial transcriptomics data, showing improved accuracy over existing methods.

Exploring the Mosaic-like Tissue Architecture of Kidney Diseases Using Relation Equivariant Graph Neural Networks on Spatially Resolved Transcriptomics

This paper introduces REGNN, a graph deep learning framework for analyzing spatially resolved transcriptomics data, showing improved performance in identifying mosaic-like tissue architectures in kidney diseases.

Speos: An ensemble graph representation learning framework to predict core genes for complex diseases

Speos predicts core genes for complex diseases using graph representation learning, achieving validation rates comparable to Mendelian disorder genes and identifying druggable targets.

Predicting gender from structural and functional connectomes via brain and population graph neural networks

This paper introduces BrainGAT, a graph neural network for gender classification using multimodal brain connectomes, achieving 83.13% accuracy on the Human Connectome Project dataset.

Graphlet-based hyperbolic embeddings capture evolutionary dynamics in genetic networks

The paper introduces GraCoal embedding to improve functional organization analysis of scale-free biological networks by incorporating graphlet patterns, outperforming traditional Spring embedding methods.

CONE: COntext-specific Network Embedding via Contextualized Graph Attention

CONE generates context-specific gene embeddings from context-free interactomes to identify disease-associated genes using contextualized random walks and graph attention networks.

Transfer Learning Enhanced Graph Neural Network for Aldehyde Oxidase Metabolism Prediction and Its Experimental Application

This study develops AOMP, a graph neural network for predicting AOX metabolism, outperforming benchmarks and identifying new scaffolds validated experimentally.

Graph perceiver network for lung tumor and bronchial premalignant lesion stratification from histopathology

This paper introduces GRAPE-Net, a graph-based model that stratifies bronchial premalignant lesions using histopathology images, showing improved classification accuracy for lung cancer subtypes.

Spatially Resolved Gene Expression is Not Necessary for Identifying Spatial Domains

This study introduces BINARY, a graph convolutional network using binarized spatial transcriptomics data to identify spatial domains, outperforming existing methods with less input.

A General Single-Cell Analysis Framework via Conditional Diffusion Generative Models

This paper introduces a general single-cell analysis framework using conditional diffusion models, showing competitive performance across tasks with added flexibility via prior information.

Integrating spatial transcriptomics data across different conditions, technologies, and developmental stages

This paper develops STAligner, a graph attention neural network for integrating spatial transcriptomics data across different conditions, technologies, and developmental stages, identifying shared and specific spatial domains.

Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network

This paper introduces Net-DMPred, a graph neural network-based method to predict cancer-specific driver missense mutations by incorporating molecular network structures.

Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

This paper introduces a method to transform EEG data into a spectral representation using learned graphs for motor imagery decoding, showing improved performance over existing methods.

Accurate prediction of protein tertiary structural changes induced by single-site mutations with equivariant graph neural networks

This work introduces an equivariant graph neural network to predict tertiary structural changes from single-site mutations, outperforming AlphaFold in mutant structure prediction.

Antagonists of the stress and opioid systems restore the functional connectivity of the prefrontal cortex during alcohol withdrawal through divergent mechanisms.

This study shows that CRF1 antagonist R121919 and naltrexone restore prefrontal cortex functional connectivity during alcohol withdrawal through distinct mechanisms involving modularity and connectivity changes.

Nanotope: A Graph Neural Network-Based Model for Nanobody paratope prediction

Nanotope predicts nanobody paratopes using graph neural networks and sequence/structural features, achieving higher accuracy than traditional methods without requiring antigen data.

Accurately identifying nucleic-acid-binding sites through geometric graph learning on language model predicted structures

GLMSite identifies DNA and RNA-binding sites using geometric graph learning on ESMFold-predicted structures, outperforming sequence-based methods and matching structure-based approaches.

GraphPart: Homology partitioning for biological sequence analysis

GraphPart partitions biological sequences to avoid closely related sequences in different partitions, retaining more sequences than reduction methods while maintaining homology separation quality.

Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network

This paper introduces PGCN, a structure-aware graph convolutional network for predicting protease specificity and designing proteases, showing accurate predictions and successful experimental validation.

Spatial Transcriptomics Analysis of Gene Expression Prediction using Exemplar Guided Graph Neural Network

This paper introduces EGGN, an exemplar guided graph neural network for predicting gene expression from spatial transcriptomics tissue slide images, showing superior performance on benchmark datasets.

Deep Learning of Ligand-bound RNA Tertiary Structures Diverges from Learning Unbound Ones: A Case Study Using The gRNAde Software

The paper evaluates the gRNAde software on updated RNA structure data, confirming its ability to predict sequences from structures but noting lower performance on ligand-bound RNAs like riboswitches.

SiGra: Single-cell spatial elucidation through image-augmented graph transformer

SiGra integrates spatial images and transcriptomics data to enhance spatial domain detection and improve characterization of cellular heterogeneity and communication in tissues.