graph neural network

Graph-based pangenome analysis uncovers structural and functional impacts of allopolyploidization events

The paper analyzes allopolyploidization events in Brettanomyces bruxellensis using graph pangenomes to identify structural variants and their functional impacts.

qcGEM: a graph-based molecular representation with quantum chemistry awareness

qcGEM introduces a quantum-chemistry-aware graph-based molecular representation that improves performance and interpretability in molecule-related tasks.

FlexENN: A Graph Neural Network for Binding Energy Prediction of Globular and Intrinsically Disordered Proteins

The paper introduces FlexENN, a graph neural network that predicts binding energies for both folded and intrinsically disordered protein complexes, showing improved performance over rigid-body methods.

scProfiterole: Clustering of Single-Cell Proteomic DataUsing Graph Contrastive Learning via Spectral Filters

The paper introduces scProfiterole, a framework for clustering single-cell proteomic data using graph contrastive learning with spectral filters, showing improved cell type identification.

A Graph-Attentive GAN for Rare-Cell-Aware Single-Cell RNA-Seq Data Generation

GARAGE generates realistic synthetic scRNA-seq data by addressing high-dimensional, small-sample and rare-cell challenges using graph attention and adversarial generation.

CSGDA: A Cell State-Guided Graph Domain Adaptation Network for Single-Cell Drug Response Prediction

CSGDA predicts drug responses across biological heterogeneities by addressing distribution shifts using a cell state-guided graph domain adaptation framework.

Advancing Wildlife Image Analysis: A Graph Attention Contrastive Learning Approach for Region-Specific Mammal Classification

This paper develops a region-specific AI model for mammal classification using graph attention contrastive learning, achieving high accuracy in recognizing local species and improving data cleaning efficiency.

Structurally Restricted Message-Passing within Shallow Architectures for Explainable Network-Level Brain Decoding on Small Cohorts

This paper proposes a structurally constrained message-passing framework for brain decoding on small datasets, achieving competitive performance while maintaining interpretability.

An interpretable deep learning framework for classifying neuronal morphologies using topology and graph neural networks

This paper presents a deep learning framework that classifies neuronal morphologies using topology and graph neural networks, achieving accuracy comparable to human experts.

Gaze Graphs in spatial navigation: visual behavior during free exploration explains individual differences in navigation performance

This study uses gaze graphs from eye-tracking data during virtual reality exploration to identify predictors of individual differences in spatial navigation performance.

Himito: a Graph-based Toolkit for Mitochondrial Genome Analysis using Long Reads

Himito is a graph-based toolkit for analyzing mitochondrial genomes using long reads, achieving superior performance in assembly and variant calling and revealing age-related patterns in the All of Us dataset.

Generalizable Cysteine Quantification in Pea Cultivars from SERS Spectra Using AI

The paper uses AI to quantify cysteine in pea cultivars from SERS spectra, finding that a 1D-CNN outperforms other models in accuracy and generalization.

GIN-CRC-Pareto: A graph-based Pareto-optimal multi-task learning framework to identify miRNA-target interactions in colorectal cancer

GIN-CRC-Pareto identifies miRNA-target interactions in colorectal cancer using a graph-based multi-task learning framework, outperforming existing methods in accuracy and AUC.

BioGraphX-RNA: A Universal Physicochemical Graph Encoding for Interpretable RNA Subcellular Localization Prediction

BioGraphX-RNA predicts RNA subcellular localization using physicochemical graph encoding, showing competitive performance and interpretability across mRNA, miRNA, and lncRNA.

MHCXGraph: A Graph-Based approach to detecting T cell receptor cross-reactivity

MHCXGraph identifies conserved structural regions in pMHC complexes to detect T cell cross-reactivity using graph-based analysis.

Cross-tissue Graph Attention Networks for Semi-supervised Gene Expression Prediction

This paper introduces gemGAT, a graph attention network for gene expression imputation across tissues, showing improved performance over regression-based methods on GTEx and ADNI data.

Pro-GAT: Reconnecting Fragmented PROTACs Using Graph Attention Transformer

Pro-GAT repairs chemically disconnected PROTAC candidates generated by diffusion models, improving validity while maintaining uniqueness.

Spanning-Tree Thermostatistics of Protein Allostery: An Exact Kirchhoff Framework with Application to Oncogenic KRAS

Kirchhoff matrix-tree evaluation of residue-contact spanning-tree ensembles yields exact allosteric thermodynamics; KRAS G12D conserves global and channel heat capacity but reroutes paths via Q61 and F156.

Structure-Based TCR-pMHC Binding Prediction and Generalization to Unseen Peptides

This work evaluates factors affecting generalization of GNN-based TCR-pMHC binding predictors to unseen peptides and proposes architectural improvements.

CellAwareGNN: Single-Cell Enhanced Knowledge Graph Foundation Model for Drug Indication Prediction

Adding OneK1K immune cell-type expression edges to an updated PrimeKG modestly improved drug-indication link prediction, AUPRC 0.816 to 0.826 overall and 0.847 to 0.864 for 40 autoimmune diseases.

TriGraphQA: a triple graph learning framework for model quality assessment of protein complexes

TriGraphQA improves model quality assessment of protein complexes by using a triple graph framework that distinguishes intra-chain and inter-chain features, outperforming existing methods on benchmark datasets.

A brain dynamic model based on graph neural network reflect the inter-region interaction of cortical areas

This paper develops a graph neural network model to predict brain activity and quantify inter-regional interactions, showing it can distinguish Alzheimer's patients from controls.

Predicting Enzyme pH Optima from Structure Using Equivariant Graph Neural Networks

This paper introduces pHoptNN, a graph neural network predicting enzyme pH opt from structure, achieving lower error than sequence-based methods.

DeorphaNN: Virtual screening of GPCR peptide agonists using AlphaFold-predicted active-state complexes and deep learning embeddings

DeorphaNN identifies peptide agonists for GPCRs using AlphaFold structural predictions and deep learning embeddings, successfully uncovering agonists for two orphan receptors across species.

ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures

ORIGAMI improves assessment of multimeric protein complex structures by incorporating orientation-aware geometric features into graph neural networks, achieving better performance than existing methods.

Cross-Propagative Graph Learning Reveals Spatial Tissue Domains in Multi-Modal Spatial Transcriptomics

This paper introduces st-Xprop, a graph learning method for spatial domain detection in multi-modal spatial transcriptomics, improving clustering accuracy and biological relevance.

IRCAS: a novel end-to-end approach to identify, rectify and classify comprehensive alternative splicing events in a transcriptome without genome reference

IRCAS improves reference-free alternative splicing detection in non-model organisms by integrating identification, rectification, and classification modules with specialized models.

DUET: a graph-based workflow for TCR-epitope prioritization and tumor-reactive T-cell identification

DUET improves TCR-epitope prioritization and tumor-reactive T-cell identification using a graph-based workflow, outperforming existing methods on benchmark datasets.

A Chromatin-Structure-Guided Framework for Predictive and Interpretable Regulatory Genomics

CHROME improves regulatory genomics predictions by integrating physically validated chromatin interactions into graph models, enhancing accuracy and interpretability for ChIP-seq, eQTL, and variant pathogenicity.

ACCURATE PREDICTION OF ASPARAGINE DEAMIDATION IN BIOLOGICS USING ADVANCED MACHINE LEARNING MODELS

This study develops machine learning models to predict asparagine deamidation in proteins, showing improved accuracy over existing methods and reducing development costs.