title: Genetic signatures for a rodent model of Parkinson's disease using combinatorial optimization methods.
abstract: This chapter illustrates the use of the combinatorial optimization models presented in Chapter 19 for the Feature Set selection and Gene Ordering problems to find genetic signatures for diseases using micro-array data. We demonstrate the quality of this approach by using a microarray dataset from a mouse model of Parkinson's disease. The results are accompanied by a description of the currently known molecular functions and biological processes of the genes in the signatures.
authors: Newcastle Bioinformatics Initiative, School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, New South Wales, Australia.
authors: Mou'ath
authors: M
authors: Hourani
authors: Regina
authors: R
authors: Berretta
authors: Alexandre
authors: A
authors: Mendes
authors: Pablo
authors: P
authors: Moscato
mesh_terms: D000818:Animals; D004195:Disease Models, Animal; D020869:Gene Expression Profiling; D023061:Gene Order; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis; D010300:Parkinson Disease / Q000235:genetics*; D012641:Selection, Genetic