Drug repositioning: a machine-learning approach through data integration

Drug repositioning: a machine-learning approach through data integration

: Existing computational methods for drug repositioning either rely only on the gene expression response of cell lines after treatment, or on drug-to-disease relationships, merging several information levels. However, the noisy nature of the gene expression and the scarcity of genomic data for many diseases are important limitations to such approaches. Here we focused on a drug-centered approac...

A machine learning approach to drug repositioning based on drug expression profiles: Applications in psychiatry

Development of new medications is a very lengthy and costly process. Finding novel indications for existing drugs, or drug repositioning, can serve as a useful strategy to shorten the development cycle. In this study, we present an approach to drug discovery or repositioning by predicting indication for a particular disease based on expression profiles of drugs, with a focus on applications in ...

Advancing Post-Genome Data and System Integration Through Machine Learning

Research on biological data integration has traditionally focused on the development of systems for the maintenance and interconnection of databases. In the next few years, public and private biotechnology organisations will expand their actions to promote the creation of a post-genome semantic web. It has commonly been accepted that artificial intelligence and data mining techniques may suppor...

Systematic drug repositioning through mining

Orphan/rare drug discovery through drug repositioning

There are many, often life-threatening, rare/orphan diseases for which there are few or no therapeutic options. They individually affect few people, but collectively impose very high social and economic burdens. New approaches are bringing big pharma resources to solving the problem through drug repositioning of approved drugs. Advances are being spurred by public and private partnerships, gove...