{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T21:26:54Z","timestamp":1758403614719},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"22","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,11,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Transmembrane \u03b2-barrels (TMBBs) are extremely important proteins that play key roles in several cell functions. They cross the lipid bilayer with \u03b2-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.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>Availability: The method and the cross-validation sets are available at the web page http:\/\/betaware.biocomp.unibo.it\/BetAware.<\/jats:p>\n               <jats:p>Contact: \u00a0piero.fariselli@unibo.it<\/jats:p>","DOI":"10.1093\/bioinformatics\/btr549","type":"journal-article","created":{"date-parts":[[2011,10,4]],"date-time":"2011-10-04T03:14:50Z","timestamp":1317698090000},"page":"3123-3128","source":"Crossref","is-referenced-by-count":21,"title":["Improving the detection of transmembrane \u03b2-barrel chains with N-to-1 extreme learning machines"],"prefix":"10.1093","volume":"27","author":[{"given":"Castrense","family":"Savojardo","sequence":"first","affiliation":[{"name":"1 Biocomputing Group, Department of Biology, University of Bologna CIRI-Health Science and Technology, 40126 Bologna,2Department of Computer Science, University of Bologna, 40127 Bologna, Italy"},{"name":"1 Biocomputing Group, Department of Biology, University of Bologna CIRI-Health Science and Technology, 40126 Bologna,2Department of Computer Science, University of Bologna, 40127 Bologna, Italy"}]},{"given":"Piero","family":"Fariselli","sequence":"additional","affiliation":[{"name":"1 Biocomputing Group, Department of Biology, University of Bologna CIRI-Health Science and Technology, 40126 Bologna,2Department of Computer Science, University of Bologna, 40127 Bologna, Italy"},{"name":"1 Biocomputing Group, Department of Biology, University of Bologna CIRI-Health Science and Technology, 40126 Bologna,2Department of Computer Science, University of Bologna, 40127 Bologna, Italy"}]},{"given":"Rita","family":"Casadio","sequence":"additional","affiliation":[{"name":"1 Biocomputing Group, Department of Biology, University of Bologna CIRI-Health Science and Technology, 40126 Bologna,2Department of Computer Science, University of Bologna, 40127 Bologna, Italy"}]}],"member":"286","published-online":{"date-parts":[[2011,10,3]]},"reference":[{"key":"2023012511322695700_B1","first-page":"1","article-title":"Disulfide connectivity prediction with extreme learning machines","volume-title":"In Proceeding of the International Conference on Bioinformatics Models, Methods and Algorithms.","author":"Alhamdoosh","year":"2010"},{"key":"2023012511322695700_B2","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res."},{"key":"2023012511322695700_B3","doi-asserted-by":"crossref","first-page":"W400","DOI":"10.1093\/nar\/gkh417","article-title":"PRED-TMBB: a web server for predicting the topology of beta-barrel outer membrane proteins","volume":"32","author":"Bagos","year":"2004","journal-title":"Nucleic Acids Res."},{"key":"2023012511322695700_B4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-6-7","article-title":"Evaluation of methods for predicting the topology of beta-barrel outer membrane proteins and a consensus prediction method","volume":"6","author":"Bagos","year":"2005","journal-title":"BMC Bioinformatics"},{"key":"2023012511322695700_B5","doi-asserted-by":"crossref","first-page":"W394","DOI":"10.1093\/nar\/gkh351","article-title":"BOMP: a program to predict integral \u03b2-barrel outer membrane proteins encoded within genomes of Gram-negative bacteria","volume":"32","author":"Berven","year":"2004","journal-title":"Nucleic Acids Res."},{"key":"2023012511322695700_B6","doi-asserted-by":"crossref","first-page":"2566","DOI":"10.1093\/nar\/gkh580","article-title":"Predicting transmembrane beta-barrels in proteomes","volume":"32","author":"Bigelow","year":"2004","journal-title":"Nucleic Acids Res."},{"key":"2023012511322695700_B7","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1110\/ps.0223603","article-title":"Fishing new proteins in the twilight zone of genomes: the test case of outer membrane proteins in Escherichia coli K12, Escherichia coli O157:H7, and other Gram-negative bacteria","volume":"11","author":"Casadio","year":"2003","journal-title":"Protein. 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