title: Improving the detection of transmembrane β-barrel chains with N-to-1 extreme learning machines.
abstract: BACKGROUND
  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.
authors.affiliation: Biocomputing Group, Department of Biology, University of Bologna CIRI-Health Science and Technology, 40126 Bologna, Italy.
authors.forename: Castrense
authors.initials: C
authors.lastname: Savojardo
authors.forename: Piero
authors.initials: P
authors.lastname: Fariselli
authors.forename: Rita
authors.initials: R
authors.lastname: Casadio
mesh_terms: D001185:Artificial Intelligence; D001425:Bacterial Outer Membrane Proteins / Q000737:chemistry*; D002462:Cell Membrane; D060365:Chloroplast Proteins / Q000737:chemistry*; D008565:Membrane Proteins / Q000737:chemistry; D024101:Mitochondrial Proteins / Q000737:chemistry*; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D001425:Bacterial Outer Membrane Proteins; D060365:Chloroplast Proteins; D008565:Membrane Proteins; D024101:Mitochondrial Proteins