Abstract
Schizophrenia is a frequent and devastating disorder beginning in early adulthood. Until now, the heterogeneity of this disease has been a major pitfall for identifying the aetiological, genetic or environmental factors. Age at onset or several other quantitative variables could allow for categorizing more homogeneous subgroups of patients, although there is little information on which are the boundaries for such categories. The Bayesian networks classifier approach is one of the most popular formalisms for reasoning under uncertainty. We used this approach to determine the best cut-off point for three continuous variables (i.e. age at onset of schizophrenia and neurological soft signs) with a minimal loss of information, using a data set including genotypes of selected candidate genes for schizophrenia.
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Ouali, A., Cherif, A.R., Krebs, MO. (2005). The Use of Bayesian Networks for Subgrouping Heterogeneous Diseases. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_140
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DOI: https://doi.org/10.1007/11494669_140
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
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