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Formal Concept Analysis for the Interpretation of Relational Learning applied on 3D Protein-Binding Sites

Emmanuel Bresso 1 Renaud Grisoni 1 Marie-Dominique Devignes 1 Amedeo Napoli 1 Malika Smail-Tabbone 1, * 
* Corresponding author
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Inductive Logic Programming (ILP) is a powerful learning method which allows an expressive representation of the data and produces explicit knowledge. However, ILP systems suffer from a major drawback as they return a single theory based on heuristic user-choices of various parameters, thus ignoring potentially relevant rules. Accordingly, we propose an original approach based on Formal Concept Analysis for effective interpretation of reached theories with the possibility of adding domain knowledge. Our approach is applied to the characterization of three-dimensional (3D) protein-binding sites which are the protein portions on which interactions with other proteins take place. In this context, we define a relational and logical representation of 3D patches and formalize the problem as a concept learning problem using ILP. We report here the results we obtained on a particular category of protein-binding sites namely phosphorylation sites using ILP followed by FCA-based interpretation.
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Submitted on : Sunday, October 21, 2012 - 7:00:36 AM
Last modification on : Thursday, August 4, 2022 - 5:18:43 PM
Long-term archiving on: : Friday, December 16, 2016 - 3:23:07 PM


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  • HAL Id : hal-00734349, version 1


Emmanuel Bresso, Renaud Grisoni, Marie-Dominique Devignes, Amedeo Napoli, Malika Smail-Tabbone. Formal Concept Analysis for the Interpretation of Relational Learning applied on 3D Protein-Binding Sites. 4th international conference on Knowledge Discovery and Information Retrieval - KDIR 2012, Oct 2012, Barcelona, Spain. 12 p. ⟨hal-00734349⟩



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