{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T12:01:13Z","timestamp":1767960073927,"version":"3.49.0"},"reference-count":0,"publisher":"Oxford University Press (OUP)","issue":"suppl_1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2003,7,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: All-alpha membrane proteins constitute a functionally relevant subset of the whole proteome. Their content ranges from about 10 to 30% of the cell proteins, based on sequence comparison and specific predictive methods. Due to the paucity of membrane proteins solved with atomic resolution, the training\/testing sets of predictive methods for protein topography and topology routinely include very few well-solved structures mixed with a hundred proteins known with low resolution. Moreover, available predictors fail in predicting recently crystallised membrane proteins (Chen et al., 2002). Presently the number of well-solved membrane proteins comprises some 59 chains of low sequence homology. It is therefore possible to train\/test predictors only with the set of proteins known with atomic resolution and evaluate more thoroughly the performance of different methods.<\/jats:p>\n               <jats:p>Results: We implement a cascade-neural network (NN), two different hidden Markov models (HMM), and their ensemble (ENSEMBLE) as a new method. We train and test in cross validation the three methods and ENSEMBLE on the 59 well resolved membrane proteins. ENSEMBLE scores with a per-protein accuracy of 90% for topography and 71% for topology, outperforming the best single method of 7 and 5 percentage points, respectively. When tested on a low resolution set of 151 proteins, with no homology with the 59 proteins, the per-protein accuracy of ENSEMBLE is 76% for topography and 68% for topology. Our results also indicate that the performance of ENSEMBLE is higher than that of the best predictors presently available on the Web.<\/jats:p>\n               <jats:p>Contact: gigi@biocomp.unibo.it; http:\/\/www.biocomp.unibo.it<\/jats:p>\n               <jats:p>*To whom correspondence should be addressed.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btg1027","type":"journal-article","created":{"date-parts":[[2003,7,10]],"date-time":"2003-07-10T23:49:03Z","timestamp":1057880943000},"page":"i205-i211","source":"Crossref","is-referenced-by-count":74,"title":["An ENSEMBLE machine learning approach for\nthe prediction of all-alpha membrane proteins"],"prefix":"10.1093","volume":"19","author":[{"given":"Pier Luigi","family":"Martelli","sequence":"first","affiliation":[{"name":"Laboratory of Biocomputing, CIRB\/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piero","family":"Fariselli","sequence":"additional","affiliation":[{"name":"Laboratory of Biocomputing, CIRB\/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rita","family":"Casadio","sequence":"additional","affiliation":[{"name":"Laboratory of Biocomputing, CIRB\/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2003,7,3]]},"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/19\/suppl_1\/i205\/227974","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/19\/suppl_1\/i205\/227974","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T18:45:57Z","timestamp":1674672357000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/19\/suppl_1\/i205\/227974"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2003,7,3]]},"references-count":0,"journal-issue":{"issue":"suppl_1","published-print":{"date-parts":[[2003,7,3]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btg1027","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2003,7,3]]},"published":{"date-parts":[[2003,7,3]]}}}