Abstract
Life sciences are continuously producing large amounts of complex data that require relational learning to facilitate knowledge discovery. Inductive Logic Programming (ILP) is a powerful method which allows expressive representation of the data and produces explicit knowledge. However, ILP systems return variable theories depending on heuristic user-choices of various parameters and may miss potentially relevant rules. Accordingly, we propose an original approach based on post-ILP propositionalization of the examples and Formal Concept Analysis for effective interpretation of reached rules with the possibility of adding domain knowledge. Our approach is applied to the characterization of three-dimensional (3D) protein-binding sites which are protein portions on which interactions with other proteins take place. We define a relational representation of protein 3D patches and formalize the problem as a concept learning problem using ILP. We report here the results we obtained on particular protein-binding sites namely phosphorylation sites using ILP followed by FCA-based interpretation.
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Bresso, E., Grisoni, R., Devignes, MD., Napoli, A., Smail-Tabbone, M. (2013). ILP Characterization of 3D Protein-Binding Sites and FCA-Based Interpretation. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2012. Communications in Computer and Information Science, vol 415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54105-6_6
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DOI: https://doi.org/10.1007/978-3-642-54105-6_6
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