- Title
- Genetic biomarkers for brain hemisphere differentiation in Parkinson's Disease
- Creator
- Hourani, Mou'Ath; Mendes, Alexandre; Berretta, Regina; Moscato, Pablo
- Relation
- 2007 International Symposium on Computational Models for Life Sciences (CMLS '07). Proceedings of the 2007 International Symposium on Computational Models for Life Sciences (Gold Coast, Qld 17 - 19 December, 2007) p. 207-216
- Relation
- http://cmls-conf.org/2007/CMLS07.htm
- Publisher
- American Institute of Physics
- Resource Type
- conference paper
- Date
- 2007
- Description
- This work presents a study on the genetic profile of the left and right hemispheres of the brain of a mouse model of Parkinson's disease (PO). The goal is to characterize, in a genetic basis, PO as a disease that affects these two brain regions in different ways. Using the same whole-genome microarray expression data introduced by Brown et al., we could find significant differences in the expression of some key genes, well-known to be involved in the mechanisms of dopamine production control and PD. The problem of selecting such genes was modeled as the MIN (a,/3)-FEATURE SET problem; a similar approach to that employed previously to find biomarkers for different types of cancer using gene expression microarray data. The Feature Selection method produced a series of genetic signatures for PO, with distinct expression profiles in the Parkinson's model and control mice experiments. In addition, a close examination of the genes composing those signatures shows that many of them belong to genetic pathways or have ontology annotations considered to be involved in the onset and development of PD. Such elements could provide new clues on which mechanisms are implicated in hemisphere differentiation in PD.
- Subject
- data mining; feature selection; microarray data analysis
- Identifier
- http://hdl.handle.net/1959.13/921309
- Identifier
- uon:9283
- Identifier
- ISBN:9780735404663
- Language
- eng
- Full Text
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