title: Top scoring pair decision tree for gene expression data analysis.
abstract: Classification problems of microarray data may be successfully performed with approaches by human experts which are easy to understand and interpret, like decision trees or Top Scoring Pairs algorithms. In this chapter, we propose a hybrid solution that combines the above-mentioned methods. An application of presented decision trees, which splits instances based on pairwise comparisons of the gene expression values, may have considerable potential for genomic research and scientific modeling of underlying processes. We have compared proposed solution with the TSP-family methods and decision trees on 11 public domain microarray datasets and the results are promising.
authors: Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland. m.czajkowski@pb.edu.pl
authors: Marcin
authors: M
authors: Czajkowski
authors: Marek
authors: M
authors: Krȩtowski
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics; D004273:DNA, Neoplasm / Q000235:genetics; D003627:Data Interpretation, Statistical; D030561:Databases, Nucleic Acid; D003663:Decision Trees; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*
chemical_list: D004273:DNA, Neoplasm