AIMC Topic: Graph Neural Networks

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Path-Based Graph Neural Network for Drug Synergy Prediction and Interpretation.

Journal of chemical information and modeling
Combination therapy is a method of treating complex diseases by using multiple drugs, which has the advantages of good efficacy and few toxic side effects. It has been widely used in clinical research. The significant increase in the number of drug c...

AI-powered IC50 prediction for p53 inhibitors drug-target interaction via hybrid graph neural networks.

Journal of computer-aided molecular design
In recent decades, the rapid pace of digital transformation marks a transformative era for the healthcare and pharmaceutical industries. The incorporation of innovative technology, specifically Artificial Intelligence (AI) and its derivatives, has dr...

PERMA-guided multi-topology graph neural networks for cross-cultural student well-being prediction.

PloS one
Student well-being prediction is of great significance for promoting personalized education and preventing mental health problems, but existing methods suffer from limitations including lack of psychological theory guidance, neglect of student relati...

Temporal social network modeling of mobile connectivity data with graph neural networks.

PloS one
Graph neural networks (GNNs) have emerged as a state-of-the-art data-driven tool for modeling connectivity data of graph-structured complex networks and integrating information of their nodes and edges in space and time. However, as of yet, the analy...

Multi-scale dynamic graph neural network for PM2.5 concentration prediction in regional station cluster.

PloS one
Accurate prediction of PM2.5 concentrations is crucial for public health and environmental management. However, effectively capturing complex spatiotemporal dependencies across multiple time scales remains a persistent challenge for existing methods,...

Integrating graph neural networks and LSTM for path optimization in smart port multi-modal systems.

PloS one
This paper addresses the challenges of dynamic environments and multimodal data fusion in multimodal transport path optimization for smart ports by proposing a GL-SSL Model that integrates Graph Neural Networks (GCN), Long Short-Term Memory (LSTM), a...

An interpretable geometric graph neural network for enhancing the generalizability of drug-target interaction prediction.

BMC biology
BACKGROUND: Accurate prediction of drug-target interactions (DTIs) is essential for advancing drug discovery. Although numerous computational methods have been proposed, many exhibit limited generalization, particularly when dealing with unseen drugs...

Cortical surface electric field estimation for real-time TMS with graph neural networks.

Physics in medicine and biology
Transcranial magnetic stimulation is a non-invasive neurostimulation and neuromodulation technique that induces electric fields (-fields) in the brain via a coil placed over the scalp. Our objective is to develop a real-time estimation method for the...

A graph neural network model for inferring interindividual variation from experimental biological data.

Scientific reports
Interindividual variation in biological responses to physiological stimuli is a widely recognized phenomenon. However, effective computational tools for identifying the individual-specific mechanisms remain limited. We present the bioreaction-variati...

DeepEGFR a graph neural network for bioactivity classification of EGFR inhibitors.

Scientific reports
Epidermal Growth Factor Receptor (EGFR) plays a critical role in the development of several cancers. Thus, modulation/inhibition of EGFR activity is an appealing target of developing novel cancer therapeutics. With the advent of modern machine learni...