graph neural network

Assessing combinatorial diversity of aureochrome bZIPs through genome-wide screening

This study assesses the combinatorial diversity of aureochrome bZIPs by analyzing dimerization partners and stability using bioinformatics and network science methods.

Advancing Antibiotic Resistance Classification with Deep Learning Using Protein Sequence and Structure

This paper develops two deep learning models using protein sequence and structure to classify antibiotic resistant genes, achieving over 73% accuracy across 18 categories.

A graph-matching based metric of functional connectome distance between pairs of individuals varies with their ages, cognitive performances and familial relationships.

This study introduces a graph-matching based metric to quantify functional connectome differences between individuals and finds it correlates with age, cognitive performance, and familial relationships.

Interpretable brain decoding from sensations to cognition to action: graph neural networks reveal the representational hierarchy of human cognition

This paper introduces biologically-constrained graph neural networks to decode cognitive processes, revealing functional gradients from sensation to cognition and showing improved inter-subject alignment and behavioral prediction.

DENVIS: scalable and high-throughput virtual screening using graph neural networks with atomic and surface protein pocket features

DENVIS is a virtual screening method using graph neural networks that outperforms docking-based and sequence-based models by avoiding docking steps and using atomic and surface protein features.

Identification of spatially variable genes with graph cuts

This paper introduces scGCO, a method for identifying spatially variable genes in single-cell data, showing superior performance and scalability.

Multi-omics data integration via novel interpretable k-hop graph attention network for signaling network inference

This study introduces a novel interpretable k-hop graph attention network to integrate multi-omics data and infer essential targets and signaling networks, showing improved performance over existing models.

Graph-based algorithms for Laplace transformed coalescence time distributions.

This paper introduces graph-based algorithms to efficiently compute Laplace transformed coalescence time distributions, improving computational efficiency for population genetic analyses.

Struct2Graph: A graph attention network for structure based predictions of protein-protein interactions

Struct2Graph predicts protein-protein interactions using 3D structural data with high accuracy and identifies key residues involved in interactions.

Mass Enhanced Node Embeddings for Drug Repurposing

The paper introduces a node embedding algorithm for drug repurposing that outperforms walk-based methods by 53.33% in average precision.

omicsGAT: Graph Attention Network for Cancer Subtype Analyses

omicsGAT integrates graph-based learning with attention mechanisms to improve cancer subtype analysis using RNA-seq data.

Multichannel neural spike sorting with spike reduction and positional feature

The paper introduces GEMsort, a multichannel neural spike sorting algorithm that reduces data volume and improves sorting speed using signal processing modifications.

A deep learning and digital archaeology approach for mosquito repellent discovery

The paper uses a deep learning approach to discover novel mosquito repellents by training a graph neural network on historical data and testing candidates in behavioral assays.

Graph neural networks ameliorate potential impacts of imprecise large-scale autonomous immunofluorescence labeling of immune cells on whole slide images

This study uses graph neural networks to improve TIL classification on whole slide images by leveraging contextual features from immunofluorescence labeling.

Alcohol dependence modifies brain networks activated during abstinence and reaccess: a c-fos-based analysis in mice

The paper uses c-fos mapping and network analysis to show that alcohol dependence alters brain network activity during abstinence and reaccess in mice, with increased network modularity in high-drinking mice after reaccess.

De novo prediction of RNA-protein interactions with Graph Neural Networks

This paper introduces a graph neural network-based method to predict RNA-protein interactions using CLIP-seq data, showing it can reconstruct interaction networks from minimal information.

A geometric deep learning framework for drug repositioning over heterogeneous information networks

This paper introduces a geometric deep learning framework for drug repositioning that leverages heterogeneous information networks to predict drug-disease associations.

Coding nucleic acid sequences with graph convolutional network

This paper introduces a graph convolutional network for analyzing nucleic acid sequences, outperforming existing methods in phage-related tasks.

Counting Is Almost All You Need

This paper shows that counting T-cell receptor repertoires outperforms existing methods in detecting CMV and HLA alleles, with an attention model and a simplified graph approach improving accuracy.

Attention-wise masked graph contrastive learning for predicting molecular property

This paper introduces a self-supervised learning method, ATMOL, for molecular property prediction using attention-wise graph masking and contrastive learning, achieving state-of-the-art performance.

Prediction of Golgi Polarity in Collectively Migrating Epithelial Cells Using Graph Neural Network

The paper uses a graph neural network to predict Golgi polarity in migrating epithelial cells, finding it correlates with stress orientation and is affected by local stress field coarsening.

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

SpaTalk infers cell-cell communication from spatial transcriptomic data using a knowledge graph and graph network, outperforming existing methods on public datasets and revealing insights into tissue communication mechanisms.

Network analysis of the human structural connectome including the brainstem: a new perspective on consciousness

The paper analyzes the human structural connectome including the brainstem, finding it to be the most highly connected structure across multiple centrality metrics.

KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data

KSTAR predicts patient-specific kinase activities from phosphoproteomic data, improving comparison across labs and identifying tissue-specific profiles and treatment responses.

Predicting compound-protein interaction using hierarchical graph convolutional networks

This paper introduces a hierarchical graph convolutional network for predicting compound-protein interactions using molecular structure and protein sequences, showing improved performance over existing methods.

Reconstructing protein interactions across time using phylogeny-aware graph neural networks

The paper introduces a phylogenetic profiling method using graph neural networks to reconstruct protein interactions across time, showing improved performance over traditional methods.

Hydrothermal Vent Species Assemblage Networks Identify Regional Connectivity Patterns in the Northwest Pacific

The paper analyzes hydrothermal vent species assemblage networks in the Northwest Pacific to identify regional connectivity patterns and their implications for conservation.

Spatial Transcriptomics Prediction from Histology jointly through Transformer and Graph Neural Networks

Hist2ST predicts RNA-seq expression from histology images using a deep learning model combining Transformer and graph neural networks, outperforming existing methods in gene expression prediction and spatial region identification.

Intrinsic linking of chromatin fiber in human cells

The paper introduces a graph-theoretic algorithm to identify CTCF-mediated chromatin loops that are linked in 3D space using ChIA-PET data, finding non-random linked regions with distinct centrality metrics.

RNANetMotif: identifying sequence-structure RNA network motifs in RNA-protein binding sites

RNANetMotif identifies sequence-structure RNA network motifs in RNA-protein binding sites using graph theory and validates them with molecular dynamics simulations.