Attention mechanism models for precision medicine.

Abstract

The development of deep learning models plays a crucial role in advancing precision medicine. These models enable personalized medical treatments and interventions based on the unique genetic, environmental and lifestyle factors of individual patients, and the promotion of precision medicine is achieved mainly through genomic data analysis, variant annotation and interpretation, pharmacogenomics research, biomarker discovery, disease typing, clinical decision support and disease mechanism interpretation. Extensive research has been conducted to address precision medicine challenges using attention mechanism models such as SAN, GAT and transformers. Especially, the recent popularity of ChatGPT has significantly propelled the application of this model type to a new height. Therefore, I propose a Special Issue for Briefings in Bioinformatics about the topic 'Attention Mechanism Models for Precision Medicine'. This Special Issue aims to provide a comprehensive overview and presentation of innovative researches on the application of graph attention mechanism models in precision medicine.