graph neural network

EquiScore: A generic protein-ligand interaction scoring method integrating physical prior knowledge with data augmentation modeling

EquiScore is a protein-ligand interaction scoring method that outperforms existing methods on external test sets and shows potential for lead compound optimization.

PLANET: A Multi-Objective Graph Neural Network Model for Protein-Ligand Binding Affinity Prediction

PLANET is a graph neural network model for protein-ligand binding affinity prediction that outperforms existing methods in virtual screening while being computationally efficient.

DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Accurate Protein-Ligand Interaction Prediction

This paper introduces DeepBindGCN, a model that predicts protein-ligand interactions using graph convolutional networks and molecular vectors, achieving high accuracy in benchmark datasets.

Brain fingerprinting using fMRI spectral signatures on high-resolution cortical graphs

This paper introduces a method using fMRI spectral signatures on high-resolution cortical graphs to improve individual identification by addressing limitations of conventional connectivity-based approaches.

Graph representations of iEEG data for seizure detection with graph neural networks

The paper proposes a data processing pipeline that generates graph representations of iEEG data and evaluates their effectiveness for seizure detection using graph neural networks.

Consensus Label Propagation with Graph Convolutional Networks for Single-Cell RNA Sequencing Cell Type Annotation

This paper introduces a GCN-based method for automating and interpreting scRNA-seq cell type annotation, showing improved accuracy over consensus and neighbor majority approaches.

Enhancing drug repurposing on graphs by integrating drug molecular structure as feature

This paper improves drug repurposing by integrating 2D drug molecular structures into graph neural networks, achieving higher AUROC and AUPRC scores than previous methods.

GraphGPSM: a global scoring model for protein structure using graph neural networks

This paper introduces GraphGPSM, a graph neural network-based scoring model for protein structure prediction, which improves accuracy over existing methods on benchmark datasets.

SIMBA: SIngle-cell eMBedding Along with features

SIMBA is a graph embedding method that jointly embeds single cells and their features into a common space, enabling diverse single-cell analysis tasks.

A Gated Graph Transformer for Protein ComplexStructure Quality Assessment and its Performancein CASP15

This paper introduces DProQA, a gated graph transformer for assessing the quality of predicted protein complex structures, achieving third place in CASP15 quality assessment.

The use of a graph database is a complementary approach to a classical similarity search for identifying commercially available fragment merges

The paper uses a graph database to identify fragment merges from chemical catalogues, finding complementary results to traditional similarity searches.

Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function

This paper introduces TransFun, a method that combines protein sequences and structures using transformers and equivariant graph neural networks to improve protein function prediction.

Supervised biological network alignment with graph neural networks

This paper introduces GraNA, a deep learning framework for supervised biological network alignment that improves functional annotation transfer between species.

Spatio-relational inductive biases in spatial cell-type deconvolution

This paper introduces GNN-C2L, a spatial cell-type deconvolution method that improves performance by incorporating spatial inductive biases, outperforming spatial-agnostic variants on simulated datasets.

Stability Oracle: A Structure-Based Graph-Transformer for Identifying Stabilizing Mutations

Stability Oracle predicts stabilizing mutations using a structure-based graph-transformer, achieving high accuracy on thermodynamic stability changes.

Exploring the utility of regulatory network-based machine learning for gene expression prediction in maize

This study evaluates regulatory network-based machine learning for gene expression prediction in maize using Arabidopsis thaliana networks, finding that simple models outperform complex ones within species but complex models are better for cross-species predictions.

ZymePackNet: rotamer-sampling free graph neural network method for protein sidechain prediction

ZymePackNet predicts protein sidechain conformations using a graph neural network without requiring rotamer libraries or energy functions, achieving high accuracy in dihedral angle prediction.

Molecule-Morphology Contrastive Pretraining for Transferable Molecular Representation

This paper introduces MoCoP, a contrastive pretraining framework that combines molecular graphs and cellular morphologies to improve QSAR model performance.

Network inference from temporal phosphoproteomics informed by protein-protein interactions

This study introduces a graph-based method to infer temporal signaling networks from high-resolution phosphoproteomics data, improving the identification of signaling-relevant interactions.

Integrated analysis of spatial multi-omics with SpatialGlue

SpatialGlue integrates spatial multi-omics data using a graph neural network to improve spatial domain detection and cross-modality correlation analysis.

Voxel-Wise Brain Graphs from Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI

The paper introduces voxel-wise brain graphs from diffusion MRI, showing that functional MRI data can be approximated by low-frequency graph Laplacian eigenmodes.

Discovery of novel multi-functional peptides by using protein language models and graph-based deep learning

This paper introduces iMFP-LG, a method combining protein language models and graph attention networks to identify multi-functional peptides, identifying 8 candidates and confirming one with antibacterial and anticancer effects.

SpatialCTD: a large-scale TME spatial transcriptomic dataset to evaluate cell type deconvolution for immuno-oncology

The paper introduces a large-scale spatial transcriptomic dataset and a Graph Neural Network-based method for cell type deconvolution in human immuno-oncology.

Spatial-linked alignment tool (SLAT) for aligning heterogenous slices properly

SLAT aligns heterogeneous spatial omics data by integrating omics profiles and spatial context, improving cell-typing resolution and regulatory inference.

SEQUENCE VS. STRUCTURE: DELVING DEEP INTO DATA DRIVEN PROTEIN FUNCTION PREDICTION

The paper compares sequence-based and structure-based methods for predicting protein function, finding that structure-based approaches can offer better accuracy when using high-quality protein structures.

MOGAT: An Improved Multi-Omics Integration Framework Using Graph Attention Networks

MOGAT improves multi-omics integration for cancer subtype prediction by addressing limitations in existing graph neural network approaches.

Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model

GraphBepi predicts B-cell epitopes using AlphaFold2 structures and a pretrained language model, outperforming existing methods in AUC and AUPR.

SUPREME: A cancer subtype prediction methodology integrating multiomics data using Graph Convolutional Neural Network

SUPREME predicts cancer subtypes using multiomics data and graph convolutions, outperforming existing methods and showing significant survival differences.

Attention-based GCN Integrates Multi-omics Data for Breast Cancer Subtype Classification and Patient-specific Gene Marker Identification

This paper introduces an attention-based GCN method for breast cancer subtype classification using multi-omics data and identifies patient-specific gene markers through interpretability techniques.

DeepDrug: A general graph-based deep learning framework for drug-drug interactions and drug-target interactions prediction

DeepDrug uses graph-based deep learning to predict drug-drug and drug-target interactions, outperforming existing methods and identifying potential SARS-CoV-2 treatments.