The Effect of Relational Background Knowledge on Learning of Protein Three-Dimensional Fold Signatures

Marcel Turcotte, Stephen H. Muggleton, and Michael J. E. Sternberg. Machine Learning, 43(1/2):81--95, April 2001.

Abstract

As a form of Machine Learning the study of Inductive Logic Programming (ILP) is motivated by a central belief: relational description languages are better (in terms of accuracy and understandability) than propo-sitional ones for certain real-world applications. This claim is investigated here for a particular application in structural molecular biology, that of constructing readable descriptions of the major protein folds. To the authors, knowledge Machine Learning has not previously been applied systematically to this task. In this application, the domain expert (third author) identified a natural divide between essentially propositional features and more structurally-oriented relational ones. The following null hypotheses are tested: I) for a given ILP system (Progol) provision of relational background knowledge does not increase predictive accuracy, 2) a good propositionallearn-ing system (C5.0) without relational background knowledge will outperform Progol with relational background knowledge, 3) relational background knowledge does not produce improved explanatory insight. Null hypotheses I) and 2) are both refuted on cross-validation results carried out over 20 of the most populated protein folds. Hypothesis 3 is refuted by demonstration of various insightful rules discovered only in the relationally-oriented learned rules.

BibTeX entry.

The Effect of Relational Background Knowledge on Learning of Protein Three-Dimensional Fold Signatures

Abstract

Other publications