The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

Accessing and integrating human genomic data with phenotypes are important for
biomedical research. Making genomic data accessible for research purposes, however …

Cross-institutional healthcare predictive modeling can accelerate research and facilitate
quality improvement initiatives, and thus is important for national healthcare delivery …

Background: There is an urgent need for the development of global analytic frameworks that
can perform analyses in a privacy-preserving federated environment across multiple …

Epistasis detection (ED) was widely used for identifying potential risk disease variants in the
human genome. A statistically meaningful ED typically requires a more extensive dataset to …

Background Data sharing is considered a crucial part of modern medical research.
Unfortunately, despite its advantages, it often faces obstacles, especially data privacy …

Logistic Regression (LR) is the most widely used machine learning model in industry for its
efficiency, robustness, and interpretability. Due to the problem of data isolation and the …

Background Artificial intelligence (AI) has been successfully applied in numerous scientific
domains. In biomedicine, AI has already shown tremendous potential, eg, in the …

Background Recent developments in machine learning have shown its potential impact for
clinical use such as risk prediction, prognosis, and treatment selection. However, relevant …

Scientific collaborations benefit from sharing information and data from distributed sources,
but protecting privacy is a major concern. Researchers, funders, and the public in general …