title: Locally linear embedding for dimensionality reduction in QSAR.
abstract: Current practice in Quantitative Structure Activity Relationship (QSAR) methods usually involves generating a great number of chemical descriptors and then cutting them back with variable selection techniques. Variable selection is an effective method to reduce the dimensionality but may discard some valuable information. This paper introduces Locally Linear Embedding (LLE), a local non-linear dimensionality reduction technique, that can statistically discover a low-dimensional representation of the chemical data. LLE is shown to create more stable representations than other non-linear dimensionality reduction algorithms, and to be capable of capturing non-linearity in chemical data.
authors.affiliation: DIRO, Université de Montreal, C.P. 6128, Succ. Centre-Ville, Montreal, Canada. lheureup@iro.umontreal.ca
authors.forename: P J
authors.initials: PJ
authors.lastname: L'Heureux
authors.forename: J
authors.initials: J
authors.lastname: Carreau
authors.forename: Y
authors.initials: Y
authors.lastname: Bengio
authors.forename: O
authors.initials: O
authors.lastname: Delalleau
authors.forename: S Y
authors.initials: SY
authors.lastname: Yue
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D007858:Learning; D016018:Least-Squares Analysis; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship
references.citation: Science. 2000 Dec 22;290(5500):2319-23
references.pmid: 11125149
references.citation: Science. 2000 Dec 22;290(5500):2323-6
references.pmid: 11125150
references.citation: J Chem Inf Comput Sci. 2000 Nov-Dec;40(6):1423-30
references.pmid: 11128101
references.citation: J Med Chem. 1991 Sep;34(9):2726-35
references.pmid: 1895293