1. Gene Expression Data Analysis Using Closed Itemset Mining for Labeled Data
- Author
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Nada Lavrač, Nataša Toplak, Petra Kralj Novak, Andrej Blejec, Kristina Gruden, Špela Baebler, and Ana Rotter
- Subjects
Models, Statistical ,Computer science ,Microarray analysis techniques ,Computational Biology ,Original Articles ,Expression (computer science) ,computer.software_genre ,Biochemistry ,Class (biology) ,Genetics ,Gene chip analysis ,Molecular Medicine ,Microarray databases ,Labeled data ,Statistical analysis ,Data mining ,DNA microarray ,Molecular Biology ,computer ,Algorithms ,Oligonucleotide Array Sequence Analysis ,Biotechnology - Abstract
This article presents an approach to microarray data analysis using discretised expression values in combination with a methodology of closed itemset mining for class labeled data (RelSets). A statistical 2 × 2 factorial design analysis was run in parallel. The approach was validated on two independent sets of two-color microarray experiments using potato plants. Our results demonstrate that the two different analytical procedures, applied on the same data, are adequate for solving two different biological questions being asked. Statistical analysis is appropriate if an overview of the consequences of treatments and their interaction terms on the studied system is needed. If, on the other hand, a list of genes whose expression (upregulation or downregulation) differentiates between classes of data is required, the use of the RelSets algorithm is preferred. The used algorithms are freely available upon request to the authors.
- Published
- 2010