title: The application of principal component analysis to drug discovery and biomedical data.
abstract: There is a neat distinction between general purpose statistical techniques and quantitative models developed for specific problems. Principal Component Analysis (PCA) blurs this distinction: while being a general purpose statistical technique, it implies a peculiar style of reasoning. PCA is a 'hypothesis generating' tool creating a statistical mechanics frame for biological systems modeling without the need for strong a priori theoretical assumptions. This makes PCA of utmost importance for approaching drug discovery by a systemic perspective overcoming too narrow reductionist approaches.
authors.affiliation: Environment and Health Department, Istituto Superiore di Sanità, Roma, Italy. Electronic address: alessandro.giuliani@iss.it.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Giuliani
mesh_terms: D000818:Animals; D001522:Behavior, Animal; D055808:Drug Discovery; D006801:Humans; D025341:Principal Component Analysis