Protein fold discovery using stochastic logic programs

Authors:
Jianzhong Chen;Lawrence Kelley;Stephen Muggleton;Michael Sternberg
Affiliations:
Department of Computing, Imperial College London, London, UK;Department of Biological Sciences, Imperial College London, London, UK;Department of Computing, Imperial College London, London, UK;Department of Biological Sciences, Imperial College London, London, UK
Venue:
Probabilistic inductive logic programming
Year:
2008

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Abstract

This chapter starts with a general introduction to protein folding. We then present a probabilistic method of dealing with multi-class classification, in particular multi-class protein fold prediction, using Stochastic Logic Programs (SLPs). Multi-class prediction attempts to classify an observed datum or example into its proper classification given that it has been tested to have multiple predictions. We apply an SLP parameter estimation algorithm to a previous study in the protein fold prediction area, in which logic programs have been learned by Inductive Logic Programming (ILP) and a large number of multiple predictions have been detected. On the basis of several experiments, we demonstrate that PILP approaches (eg. SLPs) have advantages for solving multi-class (protein fold) prediction problems with the help of learned probabilities. In addition, we show that SLPs outperform ILP plus majority class predictor in both predictive accuracy and result interpretability.