Simulating soft data to make soft data applicable to simulation.
BACKGROUND
Biomedical processes are often influenced by measures considered "non-crisp", "soft" or "subjective". Despite the growing awareness of the importance of such measures, they are rarely considered in biomedical simulation. This study introduces an input generator for soft data (input generator SD) that makes soft data applicable to simulation.


METHODS
Machine learning approaches and standard regression techniques were applied to simulate odour intensity ratings.


RESULTS
The performance of all the applied methods was satisfactory and the results can be used to modify systems biological mathematical models.


CONCLUSIONS
Soft data should no longer be discounted in systems biological simulations. Exemplarily, it can be demonstrated that the input generator SD produces results that are similar to those that the simulated system can generate. Machine learning and/or appropriate conventional mathematical approaches may be applied to simulate noncrisp processes that can be used to modify mathematical models of any granularity.
Wagner
Mathias
M
Department of Pathology, Saarland University, Homburg-Saar, Germany trouth@gmx.net
Zamelczyk-Pajewska
Malgorzata
M
Landes
Constantin
C
Sudhoff
Holger
H
Kosmider
Joanna
J
Richards
Tereza
T
Krause
Ulrike-Marie
UM
Stark
Robin
R
Groh
Andreas
A
Weichert
Frank
F
Linder
Roland
R
D000328:Adult; D003627:Data Interpretation, Statistical; D006801:Humans; D007694:Killer Cells, Natural / Q000276:immunology; D009812:Odorants; D013602:T-Lymphocytes, Cytotoxic / Q000276:immunology