Imperial College London

ProfessorMichaelSternberg

Faculty of Natural SciencesDepartment of Life Sciences

Director Centre for Bioinformatics
 
 
 
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Contact

 

+44 (0)20 7594 5212m.sternberg Website

 
 
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Location

 

306Sir Ernst Chain BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Chen:2008:10.1007/978-3-540-78652-8_9,
author = {Chen, J and Kelley, L and Muggleton, S and Sternberg, M},
doi = {10.1007/978-3-540-78652-8_9},
pages = {244--262},
title = {Protein fold discovery using stochastic logic programs},
url = {http://dx.doi.org/10.1007/978-3-540-78652-8_9},
year = {2008}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - 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. © 2008 Springer-Verlag Berlin Heidelberg.
AU - Chen,J
AU - Kelley,L
AU - Muggleton,S
AU - Sternberg,M
DO - 10.1007/978-3-540-78652-8_9
EP - 262
PY - 2008///
SN - 0302-9743
SP - 244
TI - Protein fold discovery using stochastic logic programs
UR - http://dx.doi.org/10.1007/978-3-540-78652-8_9
ER -