Disease modeling and target identification are the most crucial initial steps in drug
discovery, and influence the probability of success at every step of drug development …

Recent progress in artificial intelligence and machine learning has led to the growth of
research in every aspect of life including the health care domain. However, privacy risks and …

Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This
development has been further accelerated with the increasing use of machine learning (ML) …

Synthetic data generation is the process of using machine learning methods to train a model
that captures the patterns in a real dataset. Then new or synthetic data can be generated …

PURPOSE Synthetic data are artificial data generated without including any real patient
information by an algorithm trained to learn the characteristics of a real source data set and …

Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and
point of care applications; however, many challenges such as data privacy concerns impede …

Background One of the increasingly accepted methods to evaluate the privacy of synthetic
data is by measuring the risk of membership disclosure. This is a measure of the F1 …

Sharing data with third parties is essential for advancing science, but it is becoming more
and more difficult with the rise of data protection regulations, ethical restrictions, and growing …

With the growing demand for synthetic data to address contemporary issues in machine
learning, such as data scarcity, data fairness, and data privacy, having robust tools for …

Getting access to administrative health data for research purposes is a difficult and time-
consuming process due to increasingly demanding privacy regulations. An alternative …