Patient datasets often contain a plethora of ‘-omics’ data, but the corresponding drug response information is limited and not suitable […]
A recent phase II study used real-world evidence to assess responses to a combination treatment of atezolizumab and bevacizumab in […]
Recently, researchers have developed a machine learning approach that can predict the fitness trajectories of cancer cells in response to […]
A recent study has explored the concept of a modern and collaborative approach to data-driven drug development called Translational Precision […]
A recent study used the Human Protein Atlas as a unique big data resource to identify and prioritise candidate antibody-drug […]
A recent study, published in Drug Safety, aimed to describe the adverse drug reactions reported in inflammatory bowel disease patients […]
Researchers from the University of Texas have used data-driven approaches to identify a possible explanation for why some cancer drugs […]
Researchers at Queen Mary University of London have developed a machine learning algorithm – DRUML – that ranks drugs based […]
A recent article, published in Communications Biology, has outlined a method for expanding our understanding of host-microbiome interactions that will […]
We summarise a recent review, published in Frontiers in Microbiology, that explored how modern -omics technologies can facilitate the drug […]
