graph neural networks

PiMorph: proposal checkpoints, benchmark results and training tiles for endothelial cell complexes

Proposal checkpoints, benchmark results and training tiles for PiMorph, which reconstructs endothelial monolayers from fluorescence microscopy as embedded cell complexes: cells, gaps, cell-cell contacts and multicellular vertices with exact incidence, plus a posterior over legal complexes for every field. The code, the...

p–LSGR: A lightweight post–hoc latent–space resampling framework for imbalanced and complex graph distributions

Graph neural networks (GNNs) are widely used across domains but remain sensitive to class imbalance, class overlap, and complex data distributions, limiting reliability in real-world settings. Existing imbalance-mitigation strategies provide only partial robustness, are often computationally expensive, and leave post-h...

Transition-Aware Short-Term Traffic Congestion Prediction: Efficient Gradient Boosting versus Spatio-Temporal Graph Neural Networks

Structure-Aware Dependency Retrieval for Repository-Level Code Completion via Graph Attention

When Large Language Models (LLMs) write code inside an existing repository, the quality of their output depends heavily on the context they are given. The difficulty is that a function’s docstring rarely mentions the base class, callee, or field the function actually depends on. Lexical and dense retrievers both treat...

Structure-Aware Dependency Retrieval for Repository-Level Code Completion via Graph Attention

When Large Language Models (LLMs) write code inside an existing repository, the quality of their output depends heavily on the context they are given. The difficulty is that a function’s docstring rarely mentions the base class, callee, or field the function actually depends on. Lexical and dense retrievers both treat...

Graph Neural Network Prediction of Antioxidant Activity of Polyphenols from Grape Pomace

Grape pomace is the main solid by-product of winemaking and a cheap source of polyphenols. However, the antioxidant strength of most compounds in grape pomace has never been measured on its own. Mixtures do not behave additively, so extract results cannot be separated into compound-level values. This paper reviews the...

Phylogenies as graphs: structured neural networks improve host origin predictions from paramyxovirus sequences

A novel graph neural network approach is applied which explicitly represents viral phylogeny in model architecture to predict hosts of origin within the paramyxoviruses, a family of viruses that contains numerous endemic and emerging threats to human and animal health.

Positional Encoding Enhanced Graph Quantile Learning for Uncertainty-Aware Spatial Prediction

This work introduces the Graph Quantile Neural Network (GQNN) with Positional Encoding (PE), whichintegrates graph-based spatial representation learning, the sinusoidal positional encoding, and ordered multi-quantile regression.

ELE: Estimating tissue‐specific long noncoding RNA gene essentiality using graph neural networks

Abstract A gene is essential if its loss of function results in lethality, reduced fitness, or disease. Essential genes have attracted increasing attention due to their vital functions in biological systems. Many efforts have been made to find essential protein‐coding genes. In complex organisms, the majority of the ge...

The method of personalized foreign language teaching resource recommendation based on MTL-DQN

Dynamic and accurate resource recommendation remains a critical challenge in personalized foreign language teaching. Traditional algorithms struggle with dynamic behavior adaptation and long-tail resource neglect. An integrated framework combining multi-task learning and a deep Q-network addressed these challenges. The...

Preserving the Motif-Relevant Structure in Graph Neural Networks: An Entropy-Aware Study of Aggregation and Depth

Graph neural networks have shown strong potential for learning structural representations of biological networks. However, repeated message passing may blur local structural signals that are relevant for motif- and graphlet-based analysis. This paper investigates multilabel graphlet classification in protein–protein in...

SPECTRA: predicting cellular perturbation responses with Graph Learning over Gene Regulatory Networks

The results show that graph-based signal propagation is a biologically grounded alternative to latent-shift perturbation modeling and can improve the recovery of sparse perturbation-induced transcriptional effects.