graph neural networks

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

Accurately identifying the host of a virus from its genome sequence is a task with important applications in zoonotic disease surveillance and filling data gaps for metagenomic sampling. Machine learning approaches have seen broad application in making host predictions directly from viral genome sequences. However, mos...

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

Spatial prediction is instrumental in environmental monitoring, urban studies, and geostatistics where accuracy and dependable uncertainty bounds are mandatory. This work introduces the Graph Quantile Neural Network (GQNN) with Positional Encoding (PE), which integrates graph-based spatial representation learning, the...

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.

A Review of Chest Imaging Report Generation Enhanced by Knowledge Graphs and Graph Neural Networks

Chest imaging report generation aims to automatically generate radiology reports from images such as chest X-rays and is an important task at the intersection of medical image analysis and natural language generation. In recent years, multimodal large language models have improved the fluency and organization of genera...

Study of Mainstream Methods in Stock Price Prediction: Performance Characteristics, Comparative Analysis and Fusion Innovation Directions

Stock price prediction is a typical but difficult problem in quantitative finance that can help investors make decisions and manage risks. However, due to the high volatility of the financial market and other reasons, it cannot be known in advance. The three main methodological paradigms in the current studies are intr...