Knowledge graph embedding (KGE) is an increasingly popular technique that aims to
represent entities and relations of knowledge graphs into low-dimensional semantic spaces …

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 …

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

Prediction of drug-target interactions (DTI) plays a vital role in drug development in various
areas, such as virtual screening, drug repurposing and identification of potential drug side …

Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have
been in use in a variety of intelligent medical applications. Thus, understanding the research …

The emergence of large-scale phenotypic, genetic, and other multi-model biochemical data
has offered unprecedented opportunities for drug discovery including drug repurposing …

Conventional drug discovery and development is tedious and time-taking process; because
of which it has failed to keep the required pace to mitigate threats and cater demands of viral …

Cancer is the second leading cause of death globally, trailing only heart disease. In the
United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for …

Motivation Drug repurposing is a viable solution for reducing the time and cost associated
with drug development. However, thus far, the proposed drug repurposing approaches still …

Drug–target interaction (DTI) prediction reduces the cost and time of drug development, and
plays a vital role in drug discovery. However, most of research does not fully explore the …