Simulating Soft Data to make Soft Data applicable to Simulation

TitleSimulating Soft Data to make Soft Data applicable to Simulation
Publication TypeJournal Article
Year of Publication2006
AuthorsWagner M., Zamelczyk-Pajewska M., Landes C., Sudhoff H., Kosmider J., Richards T., Krause U.-M., Stark R., Groh A., Weichert F., Linder R.
JournalIn vivo (Athens, Greece)
Volume20
Issue1
Pages49-54
Date Published2006 Jan-Feb
Publication Languageeng
ISSN0258-851X
KeywordsAdult, Data Interpretation, Statistical, Humans, Killer Cells, Natural, Odors, T-Lymphocytes, Cytotoxic
Abstract

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.

MATERIALS AND 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.

CONCLUSION: 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.

PubMed Link

http://www.ncbi.nlm.nih.gov/pubmed/16433028?dopt=Abstract

Alternate JournalIn Vivo
Created at November 5, 2012 - 11:57am by Kulbe.

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