Abstract
In recent years amount of new information generated by biological experiments keeps growing. High-throughput techniques have been developed and now are widely used to screen biological systems at genome wide level. Extracting structured knowledge from amounts of experimental information is a major challenge to bioinformatics. In this work we propose a novel approach to analyze protein interactome data. The main goal of our research is to provide a biologically meaningful explanation for the phenomena captured by high-throughput screens. We propose to reformulate several interactome analysis problems as classification problems. Consequently, we develop a transparent classification model which while perhaps sacrificing some accuracy, minimizes the amount of routine, trivial and inconsequential reasoning that must be done by a human expert. The key to designing a transparent classification model that can be easily understood by a human expert is the use of the Inductive Logic Programming approach coupled with significant involvement of background knowledge into the classification process.
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© 2009 Springer-Verlag Berlin Heidelberg
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Jiline, M. (2009). Background Knowledge Enriched Data Mining for Interactome Analysis. In: Gao, Y., Japkowicz, N. (eds) Advances in Artificial Intelligence. Canadian AI 2009. Lecture Notes in Computer Science(), vol 5549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01818-3_43
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DOI: https://doi.org/10.1007/978-3-642-01818-3_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01817-6
Online ISBN: 978-3-642-01818-3
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