A framework for the application of decision trees to the analysis of SNPs data

Data mining is the analysis of experimental datasets to extract trends and relationships that can be meaningful for the user. In genetic studies these techniques have revealed interesting findings, especially in the heritable predisposition to contract specific diseases. One of these diseases which is still under extensive analysis is pre-eclampsia, a progressive disorder which occurs during pregnancy and soon after the birth, affecting both the mothers and their babies. There are many choices to be made in the application of the various data mining techniques that may be used to study general genotypephenotype associations. The aim of this paper is to describe the general framework that we adopted in the application of decision tree algorithms to the analysis of SNPs data related to cases of pre-eclampsia. The results show the validity of this methodology to detect a subset of attributes associated with the predictable variable, providing a reduction in the size of the dataset. Moreov...

• Type: Conference
• Year: 2009
• Where: CIBCB
• Authors: Linda Fiaschi, Jonathan M. Garibaldi, Natalio Krasnogor
• Keywords: CIBCB 2009, Computational Biology, Data Mining, Data Mining Techniques, Heritable Predisposition