graph neural network

Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural Activity

Neurospectrum extracts interpretable spatiotemporal features from neural activity using a geometric and topological deep learning framework, outperforming traditional methods in simulations and fMRI data.

Balancing Functional Tradeoffs between Protein Stability and ACE2 Binding in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB Lineages : Dynamics-Based Network Models Reveal Epistatic Effects Modulating Compensatory Dynamic and Energetic Changes

This study uses computational models to analyze how mutations in SARS-CoV-2 Omicron variants balance protein stability and ACE2 binding through epistatic interactions and dynamic changes.

Two sequence- and two structure-based ML models have learned different aspects of protein biochemistry

The paper compares sequence- and structure-based ML models for predicting protein residue properties, finding they excel at different tasks and that combining them improves accuracy.

The Effect of Node Features on GCN-based Brain Network Classification: An Empirical Study

This study evaluates how different node features affect GCN performance in brain network classification, finding that node correlation provides higher accuracy than other features.

Improved And Optimized Drug Repurposing For The SARS-CoV-2 Pandemic

The paper improves drug repurposing for SARS-CoV-2 by enhancing a graph neural network model, generating candidate drugs, and optimizing computational efficiency.

Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level

The paper introduces two deep learning models to predict carbohydrate binding sites on proteins, with one model outperforming the other and showing effectiveness on both experimental and predicted structures.

Spatial Transcriptomic Cell-type Deconvolution Using Graph Neural Networks

This paper introduces STdGCN, a graph neural network for spatial transcriptomic cell-type deconvolution, outperforming existing methods and revealing spatial patterns in cancer and heart tissue.

E2EGraph: An End-to-end Graph Learning Model for Interpretable Prediction of Pathlogical Stages in Prostate Cancer

E2EGraph predicts prostate cancer pathological stages using gene expression data and graph neural networks, achieving state-of-the-art performance with interpretable marker gene identification.

The first sheep graph-based pan-genome reveals the spectrum of structural variations and their effects on tail phenotypes

The paper constructs a graph-based sheep pan-genome to identify structural variations and their association with tail phenotypes in domestic and wild sheep.

Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST

GraphST improves spatial transcriptomics analysis by integrating clustering, integration, and deconvolution through graph self-supervised contrastive learning, achieving higher accuracy than existing methods.

Improving annotation propagation on molecular networks through random walks: Introducing ChemWalker

The paper introduces ChemWalker, a method that improves annotation propagation on molecular networks using random walks, enhancing scalability and efficiency compared to previous approaches.

Verkko: telomere-to-telomere assembly of diploid chromosomes

Verkko automates telomere-to-telomere diploid genome assembly using long-read sequencing and graph-based methods, achieving high accuracy in chromosome assembly.

A graph-based approach to identify motor neuron synergies

This paper introduces a graph-based method to estimate the number and strength of synaptic inputs to motor neurons using discharge rate correlations, validated through simulations and applied to experimental data.

An Expert-guided Hierarchical Graph Attention Network for Post-traumatic Stress Disorder Highlyassociative Genetic Biomarkers Identification

This paper develops a hierarchical graph attention network to identify PTSD-associated genetic biomarkers by integrating domain knowledge and deep learning.

In Silico Evaluation of Biomarker Genes for Melanoma Carcinoma

The paper identifies 17 up-regulated biomarkers for psoriasis-related melanoma using computational analysis of gene expression data and network biology.

scGREAT: Graph-based regulatory element analysis tool for single-cell multi-omics data

scGREAT is a tool for analyzing regulatory elements in single-cell multi-omics data, enabling heterogeneity analysis and regulatory state inference.

QC-GN2oMS2: a Graph Neural Net for High Resolution Mass Spectra Prediction.

The paper develops a graph neural network (GNN) that incorporates quantum chemical features to improve mass spectrum prediction, achieving better performance than baseline models.

Open Biomedical Network Benchmark: A Python Toolkit for Benchmarking Datasets with Biomedical Networks

The paper introduces OBNB, a benchmarking resource for GNN-based network biology tasks, providing datasets and a Python toolkit for evaluating gene classification models.

Graph Convolutional Learning of Multimodal Brain Connectome Data for Schizophrenia Classification

This paper applies graph convolutional neural networks to multimodal brain connectome data to classify schizophrenia, achieving an F1 score of 0.75.

An explainable model using Graph-Wavelet for predicting biophysical properties of proteins and measuring mutational effects

The paper introduces a graph-wavelet model to predict protein biophysical properties and assess mutational effects by analyzing amino-acid features in protein structures.

PanPA: generation and alignment of panproteome graphs

PanPA generates and aligns panproteome graphs to improve alignment accuracy in prokaryotic genomes by leveraging amino acid similarity over DNA sequence diversity.

ReTrace: Topological evaluation of white matter tractography algorithms using Reeb graphs

ReTrace evaluates tractography algorithms using Reeb graphs, showing no single algorithm consistently outperforms others and deterministic methods better capture fiber bundle properties.

Towards mechanistic models of mutational effects: Deep Learning on Alzheimer's Aβ peptide

The paper uses deep learning to model mutational effects on Aβ42 peptide aggregation traits from DMS data, showing improved performance with geometric-structural features.

Cross-species cell-type assignment of single-cell RNA-seq by a heterogeneous graph neural network

This paper introduces CAME, a heterogeneous graph neural network for cross-species cell-type assignment in scRNA-seq data, showing improved performance and revealing evolutionary patterns.

Image recovery from unknown network mechanisms forDNA sequencing-based microscopy

The paper introduces a graph-based method to reconstruct molecular networks from DNA sequencing data, achieving accurate spatial recovery with minimal assumptions.

3D-equivariant graph neural networks for protein model quality assessment

The paper introduces EnQA, a 3D-equivariant graph neural network for assessing protein model quality, achieving state-of-the-art performance and outperforming AlphaFold2's own scores.

HiC-GNN: A Generalizable Model for 3D Chromosome Reconstruction Using Graph Convolutional Neural Networks

This paper introduces HiC-GNN, a graph neural network for 3D chromosome reconstruction from Hi-C data, showing superior accuracy and generalization across resolutions and cell lines.

Simultaneous visualization of cells and marker genes from scRNA-seq studies

Stardust is a visualization method that embeds cells and marker genes together on a 2D map, performing competitively with existing tools on scRNA-seq and spatial transcriptomics data.

Graph-based pangenomics maximizes genotyping density and reveals structural impacts on fungal resistance

The paper uses graph-based pangenomics to improve genotyping density and reveal structural variation's role in fungal resistance in melon.

Integrative chromatin domain annotation throughgraph embedding of Hi-C data

The paper introduces an integrative method for chromatin domain annotation using Hi-C data and functional genomic signals without a pairwise prior, achieving better resolution of subcompartments.