title: Data reduction and representation in drug discovery.
abstract: Pre-clinical drug discovery relies increasingly on huge volumes of inter-related multivariate data. To make sense of these data and enable quality decision-making based on this plethora of information they must be presented in an interpretable form. Reducing the dimensionality of the data often leaves a data set that is too complex to interpret readily, so intuitive visualization methods are needed. Bioinformatics has provided much of the impetus for visualizing complex data, the cheminformatics community has been aggressive with the data-reduction problem. The increasing appreciation of the inter-related multifactorial nature of pre-clinical drug discovery makes visualization a burgeoning and active field that spans biosciences, mathematics and visual psychology.
authors.affiliation: Johnson & Johnson Pharmaceutical Research & Development, Janssen Pharmaceutica N.V., Turnhoutseweg 30, B-2340 Beerse, Belgium. thowe@prdbe.jnj.com
authors.forename: Trevor J
authors.initials: TJ
authors.lastname: Howe
authors.forename: Guy
authors.initials: G
authors.lastname: Mahieu
authors.forename: Patrick
authors.initials: P
authors.lastname: Marichal
authors.forename: Tom
authors.initials: T
authors.lastname: Tabruyn
authors.forename: Pieter
authors.initials: P
authors.lastname: Vugts
mesh_terms: D019295:Computational Biology / Q000379:methods*; D044962:Data Compression / Q000379:methods*; D003626:Data Display; D015195:Drug Design; D006801:Humans