title: Novel method for the display of multivariate data using neural networks.
abstract: A neural network has been used to reduce the dimensionality of multivariate data sets to produce two-dimensional (2D) displays of these sets. The data consisted of physicochemical properties for sets of biologically active molecules calculated by computational chemistry methods. Previous work has demonstrated that these data contain sufficient relevant information to classify the compounds according to their biological activity. The plots produced by the neural network are compared with results from two other techniques for linear and nonlinear dimension reduction, and are shown to give comparable and, in one case, superior results. Advantages of this technique are discussed.
authors.affiliation: SmithKline Beecham Pharmaceuticals, The Frythe, Welwyn, Herts, UK.
authors.forename: D J
authors.initials: DJ
authors.lastname: Livingstone
authors.forename: G
authors.initials: G
authors.lastname: Hesketh
authors.forename: D
authors.initials: D
authors.lastname: Clayworth
mesh_terms: D000968:Antimycin A / Q000031:analogs & derivatives / Q000737:chemistry; D003626:Data Display; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011722:Pyrethrins / Q000737:chemistry; D013329:Structure-Activity Relationship; D005680:gamma-Aminobutyric Acid / Q000031:analogs & derivatives / Q000737:chemistry
chemical_list: D011722:Pyrethrins; D005680:gamma-Aminobutyric Acid; D000968:Antimycin A