title: Predictive non-linear modeling of complex data by artificial neural networks.
abstract: An artificial neural network (ANN) is an artificial intelligence tool that identifies arbitrary nonlinear multiparametric discriminant functions directly from experimental data. The use of ANNs has gained increasing popularity for applications where a mechanistic description of the dependency between dependent and independent variables is either unknown or very complex. This machine learning technique can be roughly described as a universal algebraic function that will distinguish signal from noise directly from experimental data. The application of ANNs to complex relationships makes them highly attractive for the study of biological systems. Recent applications include the analysis of expression profiles and genomic and proteomic sequences.
authors.affiliation: Department of Biometry and Epidemiology, Medical University South Carolina, 135 Rutledge Avenue, PO Box 250551, Charleston SC 29425, USA. almeidaj@musc.edu
authors.forename: Jonas S
authors.initials: JS
authors.lastname: Almeida
mesh_terms: D001669:Biochemical Phenomena; D001671:Biochemistry; D001709:Biotechnology; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics