Data reduction and representation in drug discovery

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 leav...

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Bibliographic Details
Published in:Drug discovery today 2007, Vol.12 (1), p.45-53
Main Authors: Howe, Trevor J., Mahieu, Guy, Marichal, Patrick, Tabruyn, Tom, Vugts, Pieter
Format: Article
Language:English
Subjects:
Biological and medical sciences
Computational Biology - methods
Data Compression - methods
Data Display
Drug Design
General pharmacology
Humans
Medical sciences
Pharmaceutical technology. Pharmaceutical industry
Pharmacology. Drug treatments
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Summary: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.
ISSN:1359-6446
1878-5832
DOI:10.1016/j.drudis.2006.10.014