Facilitating Clinico-Genomic Knowledge Discovery by Automatic Selection of KDD Processes

The analysis of clinico-genomic data poses complex problems for machine learning. As high volumes of data can be generated easily, selecting the most suitable KDD-process for the problem at hand becomes increasingly hard, even for experienced researchers. The main idea of this paper is to facilitate process selection by representing each data set by a graph based on the ontology that describes data set attributes, and to apply graph mining methods to perform a similarity search. Some new measures for an effective comparison of a data set graph induced from the ontology are proposed. The effectiveness of the proposed approach is evaluated on three datasets. The results show that using ontology-based characteristics leads to improving the characterization of a data set. 1