Simple Summary Graph Neural Networks are emerging as a powerful tool for structured data
analysis, and predictive modeling in massive multimodal datasets. In this review, we survey …

Graph neural networks (GNNs) process the graph-structured data using neural networks
and have proven successful in various graph processing tasks. Currently, graph pooling …

CT-based bronchial tree analysis is a key step for the diagnosis of lung and airway
diseases. However, the topology of bronchial trees varies across individuals, which presents …

The availability of high-throughput sequencing data creates opportunities to
comprehensively understand human diseases as well as challenges to train machine …

Motivation Proteins play crucial roles in biological processes, with their functions being
closely tied to thermodynamic stability. However, measuring stability changes upon point …

The interrelation and complementary nature of multi-omics data can provide valuable
insights into the intricate molecular mechanisms underlying diseases. However, challenges …

Accurate prediction of ammonia nitrogen concentration in water is of great significance for
urban water quality management and pollution early warning. In order to improve the …

Deep learning applications have had a profound impact on many scientific fields, including
functional genomics. Deep learning models can learn complex interactions between and …

Objective The evaluation of the efficacy of immunotherapy is of great value for the clinical
treatment of bladder cancer. Graph Neural Networks (GNNs), pathway analysis and multi …

Survival prediction models play a key role in patient prognosis and personalized treatment.
However, their accuracy can be improved by incorporating patient similarity networks, which …