Drug repurposing involves the identification of new applications for existing drugs at a lower
cost and in a shorter time. There are different computational drug-repurposing strategies and …

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 …

Drug target identification is a crucial step in development, yet is also among the most
complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that …

Experimental drug development is time-consuming, expensive and limited to a relatively
small number of targets. However, recent studies show that repositioning of existing drugs …

An increased incidence of food mislabeling and handling in recent years has led to
consumers demanding transparency in how food items are produced and handled. The …

Computational prediction of drug–target interactions (DTIs) has become an essential task in
the drug discovery process. It narrows down the search space for interactions by suggesting …

A fundamental challenge in develo** high-impact machine learning technologies is
balancing the need to model rich, structured domains with the ability to scale to big data …

Link prediction is a paradigmatic problem in network science, which aims at estimating the
existence likelihoods of nonobserved links, based on known topology. After a brief …

A drug–drug interaction or drug synergy is extensively utilised for cancer treatment.
However, prediction of drug–drug interaction is defined as an ill‐posed problem, because …

Supplying a comprehensive overview of healthcare analytics research, Healthcare Data
Analytics provides an understanding of the analytical techniques currently available to solve …