MOTIVATION: Transmembrane β-barrels (TMBBs) are extremely important proteins 
that play key roles in several cell functions. They cross the lipid bilayer with 
β-barrel structures. TMBBs are presently found in the outer membranes of 
Gram-negative bacteria and of mitochondria and chloroplasts. Loop exposure 
outside the bacterial cell membranes makes TMBBs important targets for vaccine 
or drug therapies. In genomes, they are not highly represented and are difficult 
to identify with experimental approaches. Several computational methods have 
been developed to discriminate TMBBs from other types of proteins. However, the 
best performing approaches have a high fraction of false positive predictions.
RESULTS: In this article, we introduce a new machine learning approach for TMBB 
detection based on N-to-1 Extreme Learning Machines that significantly 
outperforms previous methods achieving a Matthews correlation coefficient of 
0.82, a probability of correct prediction of 0.92 and a sensitivity of 0.73.