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

A1 Originalartikel i en vetenskaplig tidskrift (referentgranskad)


Interna författare/redaktörer


Publikationens författare: Henrik Saxén, Frank Pettersson
Publiceringsår: 2010
Tidskrift: Lecture Notes in Computer Science
Tidskriftsakronym: LECT NOTES COMPUT SC
Volym: 6457
Artikelns första sida, sidnummer: 687
Artikelns sista sida, sidnummer: 695
Antal sidor: 9
ISSN: 0302-9743
eISSN: 1611-3349


Abstrakt

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).


Nyckelord

apoptosis model, data mining, Nonlinear modeling, pruning

Senast uppdaterad 2019-12-11 vid 04:53