Accurate protein structure prediction remains an active objective of research in 
bioinformatics. Membrane proteins comprise approximately 20% of most genomes. 
They are, however, poorly tractable targets of experimental structure 
determination. Their analysis using bioinformatics thus makes an important 
contribution to their on-going study. Using a method based on Bayesian Networks, 
which provides a flexible and powerful framework for statistical inference, we 
have addressed the alignment-free discrimination of membrane from non-membrane 
proteins. The method successfully identifies prokaryotic and eukaryotic 
alpha-helical membrane proteins at 94.4% accuracy, beta-barrel proteins at 72.4% 
accuracy, and distinguishes assorted non-membranous proteins with 85.9% 
accuracy. The method here is an important potential advance in the computational 
analysis of membrane protein structure. It represents a useful tool for the 
characterisation of membrane proteins with a wide variety of potential 
applications.