graph neural network

The paper identifies a structural variant upstream of KIT associated with facial depigmentation in white-headed cattle using pangenome analysis and long-read sequencing.

This study uses pangenome graphs to analyze structural variants in Brassica napus, showing they impact gene expression and eQTL analysis better than single references.

This paper introduces ska lo, a reference-free variant calling tool that uses a coloured De Bruijn graph to detect SNPs, insertions, deletions, and recombination events in pathogen genomes.

HRCHY-CytoCommunity identifies hierarchical multicellular structures in spatial cell-type maps and improves patient stratification in breast cancer.

MetDeeCINE predicts enzyme-metabolite relationships using multi-omics data and outperforms existing models in predicting concentration control coefficients in E. coli.

SpaBatch aligns spatial transcriptomics data across batches using graph deep learning to reduce technical variability and enable multi-slice integration.

This paper introduces gauge equivariant convolutional neural networks for diffusion MRI to improve angular resolution with fewer gradient directions.

The paper uses a Graph Neural Network to diagnose ALS with high accuracy and identifies keratin proteins and PT150 as potential biomarkers and therapeutics.

This paper introduces OGL, a deep learning method that integrates multi-omics data to predict gene expression and uncover regulatory interactions across cell lines and tissues.

The paper introduces AlphaPanda, an algorithm combining transformer, 3DCNN, and diffusion models for antibody sequence and structure design, achieving high-quality results with reduced training data.

Segger improves cell segmentation in spatial transcriptomics by using a graph neural network, achieving high sensitivity and speed with minimal manual intervention.

This study uses graph learning to analyze resting-state MEG data and identifies frequency-specific functional connectome differences in tinnitus patients compared to controls.

Fiora predicts compound mass spectra from single fragmentation events using graph neural networks, outperforming existing methods and enabling faster spectral library expansion.

This study uses graph theory to analyze comorbidity networks in mild TBI patients, revealing age-dependent associations between psychiatric and non-psychiatric conditions.

The paper proposes a Graph Transformer-Convolution Network (GTCN) to predict drug-cancer associations by integrating heterogeneous biological data and enhancing interpretability.

This paper introduces a 3D tracking technique to analyze social behavior in rodents, revealing new insights into autism and amphetamine effects through high-resolution postural data.

This study uses molecular dynamics and machine learning to analyze how the R591S mutation in spastin affects its dynamics and allostery, leading to impaired microtubule severing and HSP-related dysfunction.

SCOIGET infers spatial copy number variation distributions using graph neural networks, improving accuracy and efficiency in tumor evolution analysis.

This study demonstrates an AI platform that predicts and enhances stress tolerance in high-elevation extremophytes using multi-omics data and machine learning techniques.

This paper introduces LAMP features to enhance VGAEs for gene interaction network prediction, showing improved performance on missing-link prediction tasks.

This study analyzes how mutations in the NS1 protein of influenza A virus rewired allosteric and epistatic networks, enhancing its binding affinity to p85β.

stDyer improves spatial domain clustering in SRT data by combining GMVAE and GATs with dynamic graphs and mini-batch sampling, outperforming existing methods.

This study identifies small molecule binders to hnRNPA1 using MD simulations and fragment screening, revealing cryptic pockets and conformational changes.

This study links functional connectivity profiles to fluid intelligence using dimensionality reduction and graph analysis, showing that functional distance from sensory and high-cognition systems correlates with intelligence.

This paper introduces FAMPNN, a method for protein sequence design that explicitly models both amino acid identity and sidechain conformation, achieving state-of-the-art results in sidechain packing and practical design applications.

The paper introduces a graph-based method to annotate internal fragment ions in tandem mass spectra, improving proteoform sequencing and modification localization.

This paper constructs a mutation-disease dataset and proposes CGM to predict mutation-disease associations, identifying 22 unknown protein interaction pairs.

GraphMatch is a scalable graph database for allogeneic stem cell matching, demonstrating efficient search performance with large datasets.

M3NetFlow is a multi-scale multi-hop graph model for multi-omic data analysis that ranks targets and infers signaling pathways, achieving high accuracy in drug response and Alzheimer's disease studies.

The paper investigates the RNA structures and interactions of the 5' and 3' regions of the SARS-CoV-2 frameshifting element, revealing their co-regulatory roles and implications for therapeutic interventions.