Multi-Relational Learning for Genetic Data: Issues and Challenges

We present ongoing research on applying statistical relational learning techniques, in particular, propositionalization, to the challenging and interesting real-world domain of functional gene classi.cation of the Yeast genome Sachharomyces Cerevisiae. The main objective of this paper is to identify and describe the structural and statistical properties of this domain and examine how they conflict with the assumptions of the traditional relational learning approaches. Such properties are, in fact, shared by many relational application domains and potential solutions will be of interest far beyond the particular genetic application. We also report some preliminary experimental results on potential solutions for overcoming the limitations of our modeling approach by extending the existing automated feature construction strategies to accommodate the specific domain properties.

By: Claudia Perlich; Srujana Merugu

Published in: RC23678 in 2005


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