Authors:
Emmanuel Bresso
1
;
Renaud Grisoni
2
;
Marie-Dominique Devignes
3
;
Amedeo Napoli
3
and
Malika Smaïl-Tabbone
1
Affiliations:
1
Université de Lorraine, LORIA and Inria, France
;
2
Inria, France
;
3
Université de Lorraine, LORIA, Inria, CNRS and LORIA, France
Keyword(s):
Inductive Logic Programme, Formal Concept Analysis, Knowledge Discovery, 3D Protein Binding Sites.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
BioInformatics & Pattern Discovery
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Pre-Processing and Post-Processing for Data Mining
;
Soft Computing
;
Symbolic Systems
Abstract:
Inductive Logic Programming (ILP) is a powerful learning method which allows an expressive representation of the data and produces explicit knowledge in the form of a theory, i.e., a set of first-order logic rules. However, ILP systems suffer from a 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 phosphoryl
ation sites using ILP followed by FCA-based interpretation.
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