Data visualization

Computational Biology and Data Sciences (CBDS) finds novel drug targets, biomarkers, and therapeutic compounds through integrative analysis of data and knowledge.

We draw upon deep expertise in bioinformatics, data science, and machine learning to generate and interrogate in silico models of disease and therapeutic mechanism of action.

Such models are populated with ‘omics’ data from in-house experiments, as well as public and commercial data sources.

To accomplish our objectives, we build or acquire databases, visualization tools, and machine learning methods.

Targets and compounds predicted by machine learning are vetted though substantive interactions with the Therapeutic Areas, Lead Generation and Pharmacology (including computational chemistry, structural biology, and molecular pharmacology), and other functions of the Ferring Research Institute (FRI) and the broader Global Drug Discovery and External Innovation (GDDEI) organization.