A data-mining method for detection of complex nonlinear relations applied to a model of apoptosis in cell populations

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: Henrik Saxén, Frank Pettersson
Publication year: 2010
Journal: Lecture Notes in Computer Science
Journal acronym: LECT NOTES COMPUT SC
Volume number: 6457
Start page: 687
End page: 695
Number of pages: 9
ISSN: 0302-9743
eISSN: 1611-3349


Abstract

In studying data sets for complex nonlinear relations, neural networks can be used as modeling tools. Trained fully connected networks cannot, however, reveal the relevant inputs among a large set of potential ones, so a pruning of the connections must be undertaken to reveal the underlying relations. The paper presents a general method for detecting nonlinear relations between a set of potential inputs and an output variable. The method is based on a neural network pruning algorithm, which is run repetitively to finally yield Pareto fronts of solutions with respect to the approximation error and network complexity. The occurrence of an input on these fronts is taken to reflect its relevance for describing the output variable. The method is illustrated on a simulated cell population sensitized to death-inducing ligands resulting in programmed cell death (apoptosis).


Keywords

apoptosis model, data mining, Nonlinear modeling, pruning

Last updated on 2019-23-11 at 04:12