Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some
protagonists point to vast opportunities potentially offered by such tools, others remain …

In the last few years, Industrial Information Integration Engineering (IIIE) has attracted much
attention by the information and communications technology (ICT) community. However …

This book is a guide to designing and building knowledge graphs from enterprise relational
databases in practice.\It presents a principled framework centered on map** patterns to …

Abstract Interpretation of Big Data in the drug discovery community should enhance project
timelines and reduce clinical attrition through improved early decision making. The issues …

The lack of established standards to describe and annotate biological assays and screening
outcomes in the domain of drug and chemical probe discovery is a severe limitation to utilize …

Data integration is a key challenge faced in pharmacology where there are numerous
heterogeneous databases spanning multiple domains (eg chemistry and biology). To …

The use of potent and selective chemical tools with well-defined targets can help elucidate
biological processes driving phenotypes in phenotypic screens. However, identification of …

Biologists and bioinformaticians now look to ontologies or software that uses ontologies as a
means of standardising the way data are described, queried, and interpreted. Ontologies …

Business users must answer business questions quickly to address Business Intelligence
(BI) needs. The bottleneck is to understand the complex databases schemas. Only few …

A knowledge graph is a structured representation of data that can express entity and
relational knowledge. More attention has been paid to the study of a clinical knowledge …