graph neural network

SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and clustering in spatial transcriptomics

SpatialPrompt improves spot deconvolution and domain identification in spatial transcriptomics by integrating spatial coordinates and scRNA-seq data, showing scalability and accuracy across multiple datasets.

Brain fingerprinting using EEG graph inference

This paper introduces a method to estimate brain graphs using graph signal processing and demonstrates its superior performance over correlation-based functional connectomes in individual characterization.

A Principal Odor Map Unifies Diverse Tasks in Human Olfactory Perception

The paper develops a Principal Odor Map using graph neural networks that matches human-level odor description performance and generalizes to diverse odor prediction tasks.

E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction

EquiPPIS predicts protein-protein interaction sites using E(3) equivariant graph neural networks, outperforming existing methods with predicted AlphaFold2 structures.

Higher-order co-mutation interactions in mitochondrial genomes

The paper introduces a method to analyze higher-order co-mutation interactions in mitochondrial genomes using 3-uniform hypergraphs and identifies altitude-specific genes based on hyperedge weights.

A Dimensionality Reduction Approach for Motor Imagery BCI using Functional Clustering, Graph Signal Processing, and Differential Evolution

This paper introduces a dimensionality reduction approach for motor imagery BCI using functional clustering, graph signal processing, and differential evolution, achieving high classification accuracy.

Ariadne: Synthetic Long Read Deconvolution Using Assembly Graphs

Ariadne deconvolves synthetic long read data using assembly graphs to improve taxonomic classification and de novo assembly of complex populations.

Synergistic Fusion of Graph and Transformer Features for Enhanced Molecular Property Prediction

SYN-FUSION combines Graph Neural Networks and Transformers to improve molecular property prediction, outperforming existing models on several benchmarks.

MultiGML: Multimodal Graph Machine Learning for Prediction of Adverse Drug Events

This paper introduces MultiGML, a multimodal graph machine learning approach that predicts adverse drug events by integrating diverse data sources and provides explainable biological insights.

ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks

ProS-GNN predicts mutation effects on protein stability using graph neural networks, showing good generalization and low computational cost.

Predicting human microbe-drug associations via graph attention network with multiple kernel fusion

This paper introduces GATMDA, a graph attention network with multiple kernel fusion to predict human microbe-drug associations, achieving the highest AUC and AUPR on benchmark datasets.

Structure-based self-supervised learning enables ultrafast prediction of stability changes upon mutation at the protein universe scale

This paper introduces Pythia, a self-supervised graph neural network for fast and accurate prediction of mutation-induced stability changes across protein structures, showing superior performance and speed.

DeltaAI: Semi-Autonomous Tissue Grossing Measurements and Recommendations using Neural Radiance Fields for Rapid, Complete Intraoperative Histological Assessment of Tumor Margins

DeltaAI uses Neural Radiance Fields to create 3D tissue models for faster and more accurate intraoperative tumor margin assessment in Mohs Micrographic Surgery.

Predicting Protein-Ligand Binding Structure Using E(n) Equivariant Graph Neural Networks

The paper introduces an E(n) Equivariant Graph Neural Network to predict ligand-protein binding structures, showing promising results for drug design.

SpaRx: Elucidate single-cell spatial heterogeneity of drug responses for personalized treatment

SpaRx identifies spatial cellular heterogeneity in drug responses across tumor lesions and reveals mechanisms of drug resistance through a graph-based domain adaptation model.

Chrombus-XMBD: A Graph Generative Model Predicting 3D-Genome, ab initio from Chromatin Features

Chrombus-XMBD predicts 3D-genome structures from chromatin features using a graph generative model, outperforming existing methods and showing cross-species generalizability.

De novo Protein Sequence Design Based on Deep Learning and Validation on CalB Hydrolase

The paper designs novel protein sequences using a Graphormer-based model and validates them on CalB hydrolase, achieving 1.7x higher specific activity than the wild type.

Graphical Learning and Causal Inference for Drug Repurposing

The paper introduces a causal inference and graph neural network framework for drug repurposing, demonstrating its effectiveness through simulations and application to L1000 connectivity map data.

Graph Attention Site Prediction (GrASP): Identifying Druggable Binding Sites Using Graph Neural Networks with Attention

GrASP identifies druggable protein binding sites using graph neural networks with attention, achieving state-of-the-art performance and high precision.

Kmer-Node2Vec: a Fast and Efficient Method for Kmer Embedding from the Kmer Co-occurrence Graph, with Applications to DNA Sequences

The paper introduces Kmer-Node2Vec, a fast graph-based method for kmer embedding in DNA sequences, achieving faster training times than DNA2Vec while maintaining comparable accuracy.

VoroIF-GNN: Voronoi tessellation-derived protein-protein interface assessment using a graph neural network

VoroIF-GNN assesses protein-protein interface accuracy using a graph neural network based on Voronoi tessellation, showing strong performance in CASP15.

Hypergraph factorisation for multi-tissue gene expression imputation

HYFA improves multi-tissue gene expression imputation by leveraging hypergraphs and transfer learning, outperforming existing methods and enabling cell-type signature resolution from bulk data.

cycle_finder: de novo analysis of tandem and interspersed repeats based on cycle-finding

cycle_finder detects both tandem and interspersed repeats from short-read data without reference sequences, outperforming existing tools in recall and precision.

Learning Complete Protein Representation by Deep Coupling of Sequence and Structure

This paper introduces CoupleNet, a model that integrates protein sequence and structure to improve representation learning, achieving better performance in fold classification and function prediction.

Eulertigs: minimum plain text representation of k-mer sets without repetitions in linear time

The paper introduces an algorithm to compute a minimum plain text representation of k-mer sets without repetitions in linear time and evaluates existing heuristics.

Inferring Neuron-level Brain Circuit Connection via Graph Neural Network Amidst Small Established Connections

The paper introduces NCPNet, a graph neural network for inferring neuron-level brain connections using limited training data, achieving high accuracy on Drosophila and C. elegans connectomes.

GRIP: Graph representation of immune repertoire using graph neural network and transformer

GRIP improves cancer patient survival prediction by modeling immune repertoires as hierarchical graphs, outperforming set-based methods and providing interpretable results.

Interpretable Chirality-Aware Graph Neural Network forQuantitative Structure Activity Relationship Modeling in Drug Discovery

The paper introduces a chirality-aware graph neural network for drug discovery that improves predictive performance and interpretability in quantitative structure activity relationship modeling.

Mixed structure- and sequence-based approach for protein graph neural networks with application to antibody developability prediction

This paper introduces a graph neural network for antibody developability prediction using structure- and sequence-based methods, achieving state-of-the-art performance.

Investigating mitochondrial gene expression patterns in Drosophila melanogaster using network analysis to understand aging mechanisms

This study uses network analysis in Drosophila melanogaster to identify mitochondrial genes associated with aging and age-related diseases.