Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …

With the growth of data and intelligent technologies, the healthcare sector opened numerous
technology that enabled services for patients, clinicians, and researchers. One major hurdle …

Developing personalized diagnostic strategies and targeted treatments requires a deep
understanding of disease biology and the ability to dissect the relationship between …

With the worldwide digitalisation of medical records, electronic health records (EHRs) have
become an increasingly important source of real-world data (RWD). RWD can complement …

The field of oncology has witnessed an extraordinary surge in the application of big data and
artificial intelligence (AI). AI development has made multiscale and multimodal data fusion …

Objective Integrating and harmonizing disparate patient data sources into one consolidated
data portal enables researchers to conduct analysis efficiently and effectively. Materials and …

Objective: The growing availability of electronic health records (EHR) data opens
opportunities for integrative analysis of multi-institutional EHR to produce generalizable …

Disease knowledge graphs have emerged as a powerful tool for artificial intelligence to
connect, organize, and access diverse information about diseases. Relations between …

Motivation Predicting molecule–disease indications and side effects is important for drug
development and pharmacovigilance. Comprehensively mining molecule–molecule …

Transfer learning is crucial for training models that generalize to unlabeled target
populations using labeled source data, especially in real-world studies where label scarcity …