graph neural network

Comparative analysis identifies a shared topological organisation among divergent intergenic RNAs

The paper identifies a shared topological organisation in divergent arenavirus intergenic RNAs using graph theory and structural analysis.

Core Coefficient quantifies transcription-factor functional dominance for therapeutic target selection

The paper introduces the Core Coefficient (CC) to quantify transcription factor functional dominance in regulatory networks, showing it can predict therapeutic target shifts in disease progression.

A rare pre-existing progenitor-like Primed SMC compartment is the dominant inferred source of SMC-derived cellularity in vascular injury and atherosclerosis

Phenotype-cloud reorganization shapes cancer progression and therapeutic adaptation

This study identifies phenotype-cloud transitions as an organizational feature of cancer progression and therapeutic adaptation, showing how tumours reorganize phenotypic states during progression and treatment.

geoGraph: Connectivity modelling across geographic space with hexagonal discrete global grids

geoGraph models organism movement and connectivity across geographic space using hexagonal grids, improving accuracy over rectangular grids by avoiding distortion.

Learning Dynamic Protein Representations at Scale with Distograms

Adding Boltz2 distogram edges and features to protein and RNA graph networks beats MD-correlation graphs on binding tasks and improves stability, interaction and RNA predictions without simulation.

AI-Driven Design of Next-Generation Immunoinformatics Multi-Epitope Subunit Vaccine Targeting the Most Virulent Mpox Proteome: A Structural and Kinetic Validation Framework

This paper develops an AI-driven vaccine design framework for Mpox, yielding three multi-epitope constructs validated for structural stability, immune binding, and expression efficiency.

IsoGraph resolves coordinated transcript choice and gene abundance in the aging human brain

IsoGraph identifies age-associated transcript-usage signals in human brain transcriptomes, distinguishing them from gene abundance and cellular composition effects.

GenDiMug: a generic inclusive multigraph framework to analyze genomic organizations and -omics data at multiple scales

GenDiMug is a multigraph framework for analyzing genomic organization and multi-omics data across scales, revealing gene expression clumping and circadian regulation in a photosynthetic alga.

Tractable and Thermodynamically Consistent pKa Prediction via First-Dissociation Aggregation

This paper introduces DTi-pKa, a graph neural network that predicts pKa values with thermodynamic consistency and achieves high accuracy on benchmark datasets.

Reliable Evaluation of Transductive Population Graph Neural Networks for Multisite Autism fMRI

Site-aware population GNN on ABIDE-I: cohort AUC about 0.94 (kept with site-only edges) was not reproduced by a site-prior classifier (0.506); a directed-edge support gain (+0.252) vanished when symmetrised (−0.003).

BiomiX 3.0: A user-friendly platform for democratized multi-omics integration with graph-based learning.

BiomiX 3.0 is a multi-omics integration platform that introduces PRAMIGO, a supervised heterogeneous graph transformer, and benchmarks five methods on CLL and PTB datasets, showing that no single method captures all biologically relevant signals.

Learning Environment-Associated Marker Rankings with Attention-Based Graph Neural Networks

This paper introduces a graph neural network to estimate environment-associated marker rankings from crop trial data and evaluates its performance on maize datasets.

Somatic haplotype reconstruction and variant recalibration from tumor-only long-read sequencing

LongPhase-TO reconstructs somatic haplotypes from tumor-only long-read sequencing, improving variant calling accuracy and haplotype block size.

Structured cross-omics interaction discovery with a triple-graph model

The paper introduces TriGer, a triple-graph model that identifies cross-omics modules by combining cross-layer associations with dependency structures, showing effectiveness in inflammatory bowel disease and colorectal cancer.

TLS-Sim: An Open-Source Toolkit for Individualized Transcranial Light Stimulation Modeling with Deep-Learning-Accelerated Simulation

TLS-Sim provides a toolkit for individualized transcranial light stimulation modeling using deep-learning-accelerated Monte Carlo simulations to improve efficiency and accuracy.

A heterogeneous biomedical knowledge network framework for rare disease drug candidate prioritization: integrating Orphadata and DisGeNET via gene-bridge harmonization

This paper develops a network framework to prioritize drug candidates for rare diseases by integrating Orphadata and DisGeNET through gene-bridge harmonization and using a Graph Attention Network for node representation learning.

A network-based framework for detecting communities of similar neural spike trains

This paper introduces a network-based method to detect neuronal assemblies from spike train data using the van Rossum distance and Louvain algorithm, validated on synthetic and real datasets.

Benchmark validity in graph neural network scoring of metabolic reaction activity on Recon3D: detecting label leakage, memorized noise and input-invariant models

The paper benchmarks a graph neural network for scoring metabolic reaction activity in tumors but finds issues like label leakage and memorization, leading to retraction of previous results.

Pangenome alignment reveals global diversity and evolution of human centromeric regions

The paper introduces Centrolign, a tool for aligning centromeric regions, revealing mutation dynamics and enabling variant calling in human centromeres.

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.

Atlantis: An integrative database for human proteome structural and functional sites

Atlantis integrates structural and functional residue-level data for the human proteome, enabling complex queries and analysis of protein interactions and mutations.

Lineage-specific X chromosome inactivation escape and skew underlie sex-biased immune gene dosage and deleterious variant exposure

This paper introduces scDaisyChain to analyze X chromosome inactivation escape and skew in immune cells, revealing lineage-specific effects on gene dosage and variant exposure in healthy and autoimmune disease contexts.

Benchmarking Graph Neural Networks for Multi-Omics Cancer Subtyping using Methylation and Gene Expression Profiles

This study benchmarks graph neural networks for cancer subtyping using methylation and gene expression data, finding that ChebNet performs best and simple readout layers improve performance.

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.

InterPET: A Curated Benchmark of Sequence Embeddings and Graph Architectures with Interpretability and Biological Validation for PETase Activity Prediction

The paper introduces InterPET, a benchmark for predicting PETase activity using sequence embeddings and graph architectures, showing that model performance and biological validity are interdependent.

Mapping disease traits onto spatiotemporal domain landscapes through scalable multi-sample integration of spatial transcriptomics

STUltra integrates spatial transcriptomics data to map disease traits onto spatiotemporal tissue landscapes, outperforming existing methods in scalability and speed.

DNA2Graph enables automated identification of non-linear DNA molecules in electron microscopy

DNA2Graph automates the identification of non-linear DNA molecules in electron microscopy images, significantly reducing the need for manual review and improving structure detection.

Multi-modal Graph Integration for Biologically Interpretable Domain Identification Using Spatial Transcriptomics

This study introduces BIGraph-ST, a method that integrates pathway activity and histological features to improve spatial domain identification in spatial transcriptomics, showing better performance in cancer tissues.

MEGA-ODE: Learning Biologically Structured and Navigable Continuous Perturbation Dynamics from Sparse Omics

MEGA-ODE reconstructs dynamic omics landscapes from sparse data, predicts unmeasured states, and prioritizes perturbations toward biological endpoints.