graph neural network

OmicVerse: A single pipeline for exploring the entire transcriptome universe

OmicVerse integrates bulk and single-cell RNA-seq analysis with a pipeline that restores continuity in interrupted single-cell data using Beta-Variational AutoEncoder and graph neural networks.

Supervised Deep Learning with Gene Annotation for Cell Classification

The paper introduces SDAN, a deep learning method that integrates gene annotations with scRNA-seq data to improve cell classification and gene set identification, outperforming existing methods in three applications.

Efficient and scalable prediction of spatio-temporal stochastic gene expression in cells and tissues using graph neural networks

This paper develops a graph neural network method to predict spatio-temporal stochastic gene expression efficiently, achieving faster computation and extrapolation capabilities.

Network-based integrative multi-omics approach reveals biosignatures specific to COVID-19 disease phases.

This paper identifies biosignatures specific to different COVID-19 disease phases through integrative multi-omics network analysis and finds that disease severity correlates with increased biosignature interactions across omics data.

G-PLIP: Knowledge graph neural network for structure-free protein-ligand bioactivity prediction

The paper introduces a graph neural network for predicting protein-ligand interactions without structural data, achieving competitive performance using representation learning.

RevGraphVAMP: A protein molecular simulation analysis model combining graph convolutional neural networks and physical constraints

RevGraphVAMP improves protein simulation analysis by combining graph neural networks and physical constraints, achieving higher accuracy in dimensionality reduction and state transition prediction.

A graph-based practice of evaluating collective identities of cell clusters

The paper improves a graph-based method for evaluating cell cluster identities by addressing practical implementation issues and enhancing utility with new features and techniques.

SCREEN: a graph-based contrastive learning tool to infer catalytic residues and assess mutation tolerance in enzymes

SCREEN is a graph neural network that predicts catalytic residues and assesses mutation tolerance in enzymes by integrating structural and functional information.

Network approach reveals preferential T-cell and macrophage association with α-linked β-cells in early stage of insulitis in NOD mice.

The paper uses network analysis to show that in early insulitis, immune cells preferentially interact with β-cells in specific regions of pancreatic islets in NOD mice.

ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries

ADMET-AI, a Chemprop-RDKit graph neural network trained on 41 TDC datasets, has the best average leaderboard rank and scores one million molecules in 3.1 hours.

Predicting Spatially Resolved Gene Expression via Tissue Morphology using Adaptive Spatial GNNs

This paper predicts spatial gene expression from histological images using adaptive spatial GNNs, improving performance on breast cancer data.

PEPerMINT: Peptide Abundance Imputation in Mass Spectrometry-based Proteomics using Graph Neural Networks

PEPerMINT imputes missing peptide abundance values in proteomics using graph neural networks, outperforming existing methods across multiple datasets and missingness scenarios.

PSICHIC: physicochemical graph neural network for learning protein-ligand interaction fingerprints from sequence data

PSICHIC predicts protein-ligand interactions from sequence data using a physicochemical graph neural network, achieving state-of-the-art accuracy and interpretability in binding affinity and functional effect prediction.

Path-based reasoning in biomedical knowledge graphs with BioPathNet

BioPathNet improves link prediction in biomedical knowledge graphs by using path-based reasoning, outperforming existing methods and identifying novel drug indications and gene interactions.

Graspot: A graph attention network for spatial transcriptomics data integration with optimal transport

Graspot integrates spatial transcriptomics data using a graph attention network with optimal transport, improving alignment across datasets and revealing tumor microenvironment structures in breast cancer.

Emerging Drug Combinations for Targeting Tongue Neoplasms Associated Proteins/Genes: Employing Graph Neural Networks within the RAIN Protocol

This paper identifies optimal drug combinations for tongue neoplasms using a Graph Neural Network within the RAIN protocol, finding Cisplatin, Bleomycin, and Fluorouracil as effective treatments.

Mutation Pathogenicity Prediction by a Biology Based Explainable AI Multi-Modal Algorithm

This paper introduces TriVIAI, an explainable AI tool for predicting missense mutation pathogenicity using three modalities, achieving high performance metrics and offering biological insights and explainability.

Probabilistic graph-based model uncovers previously unseen druggable vulnerabilities in major solid cancers

This paper introduces a graph-based model to identify druggable vulnerabilities in solid cancers, finding 43 novel targets by leveraging cooperativity in molecular networks.

AMPGLDA: Predicting LncRNA-Disease Associations Based on Adaptive Meta-Path Generation and Multi-Layer Perceptron

This paper introduces AMPGLDA, a method predicting lncRNA-disease associations using adaptive meta-path generation and a deep neural network, achieving high AUC and AUPR scores.

PNAbind: Structure-based prediction of protein-nucleic acid binding using graph neural networks

PNAbind predicts protein-nucleic acid binding using graph neural networks, achieving high accuracy in binding site prediction and discrimination between DNA and RNA specificity.

Dimensionality Reduction and Denoising of Spatial Transcriptomics Data Using Dual-Channel Masked Graph Autoencoder

The paper introduces STMask, a graph self-supervised learning method for spatial transcriptomics data, improving spatial clustering and trajectory inference.

PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications

PathFinder infers multi-cell signaling pathways and communications using a graph transformer model, identifying novel interactions in AD mouse models.

Improving Cell-type-specific 3D Genome Architectures Prediction Leveraging Graph Neural Networks

This paper introduces GRACHIP, a graph neural network model that improves prediction of cell-type-specific 3D genome architectures by incorporating genomic interaction information, showing better accuracy and generalizability than existing methods.

CellWalker2: multi-omic discovery of hierarchical cell type relationships and their associations with genomic annotations

CellWalker2 improves cell type annotation and mapping by integrating multi-omic data and hierarchical relationships, outperforming existing methods in simulations and demonstrating applications in brain and immune system data.

Hypergraph Cortical Cytoarchitectonic Parcellation with Multimodal Canine Brain Atlas

The paper constructs a multimodal canine brain atlas and introduces a hypergraph-based method for fine-grained cortical cytoarchitectonic partitioning.

Spatial domains identification in spatial transcriptomics by domain knowledge-aware and subspace-enhanced graph contrastive learning

This paper introduces GRAS4T, a graph contrastive learning framework for spatial domain identification in spatial transcriptomics, showing improved performance over existing methods.

Predicting transcriptional activation domain function using Graph Neural Networks

The paper predicts transcriptional activation domain function using graph neural networks, achieving high accuracy and outperforming existing methods.

A hepatitis B virus (HBV) sequence variation graph improves sequence alignment and sample-specific consensus sequence construction for genetic analysis of HBV

This paper shows that using a genome graph instead of a linear reference improves HBV sequence alignment and sample-specific consensus sequence construction.

Graph convolutional network learning model based on new integrated data of the protein-protein interaction network and network pharmacology for the prediction of herbal medicines effective for the treatment of metabolic diseases

This study uses a graph convolutional network to predict effective herbal prescriptions for metabolic diseases by integrating protein-protein interaction networks and network pharmacology data.

Genome-scale annotation of protein binding sites via language model and geometric deep learning

GPSite predicts protein binding sites across multiple ligands using a multi-task network, outperforming existing methods and enabling genome-scale annotations.