{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T06:23:07Z","timestamp":1743056587488,"version":"3.40.3"},"publisher-location":"Berlin, Heidelberg","reference-count":33,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642200380"},{"type":"electronic","value":"9783642200397"}],"license":[{"start":{"date-parts":[[2011,1,1]],"date-time":"2011-01-01T00:00:00Z","timestamp":1293840000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011]]},"DOI":"10.1007\/978-3-642-20039-7_54","type":"book-chapter","created":{"date-parts":[[2011,4,16]],"date-time":"2011-04-16T07:37:49Z","timestamp":1302939469000},"page":"538-547","source":"Crossref","is-referenced-by-count":0,"title":["The Application of Fusion of Heterogeneous Meta Classifiers to Enhance Protein Fold Prediction Accuracy"],"prefix":"10.1007","author":[{"given":"Abdollah","family":"Dehzangi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roozbeh Hojabri","family":"Foladizadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Aflaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sasan","family":"Karamizadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"54_CR1","doi-asserted-by":"crossref","unstructured":"Shi, S.Y.M., Suganthan, P.N., Deb, K.: Multi class protein fold recognition using multi-objective evolutionary algorithms. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 61\u201366 (2004)","DOI":"10.1109\/CIBCB.2004.1393933"},{"key":"54_CR2","doi-asserted-by":"crossref","unstructured":"Dehzangi, A., Khosravi, B.G.: Introducing Novel Physicochemical Based Features to Enhance Protein Fold Prediction Accuracy. In: Proceeding in: IEEE International Conference on Computer Design and Applications, pp. 592\u2013596 (2010)","DOI":"10.1109\/ICCDA.2010.5540884"},{"issue":"1","key":"54_CR3","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1109\/TNB.2009.2016488","volume":"8","author":"P. Ghanty","year":"2009","unstructured":"Ghanty, P., Pal, N.R.: Prediction of Protein Folds: Extraction of New Features, Dimensionality Reduction, and Fusion of Heterogeneous Classifiers. IEEE Transaction on Nanobioscience\u00a08(1), 100\u2013110 (2009)","journal-title":"IEEE Transaction on Nanobioscience"},{"issue":"2","key":"54_CR4","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1109\/TNB.2007.897482","volume":"6","author":"K.L. Lin","year":"2008","unstructured":"Lin, K.L., Li, C.Y., Huang, C.D., Chang, H.M., Yang, C.Y., Lin, C.T., Tang, C.Y., Hsu, D.F.: Feature Selection and Combination Criteria for Improving Accuracy in Protein Structure Prediction. IEEE Transactions on Nanobioscience\u00a06(2), 186\u2013196 (2008)","journal-title":"IEEE Transactions on Nanobioscience"},{"issue":"14","key":"54_CR5","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1093\/bioinformatics\/btl170","volume":"22","author":"H.B. Shen","year":"2006","unstructured":"Shen, H.B., Chou, K.C.: Ensemble Classifier for Protein Fold Pattern Recognition. Bioinformatics\u00a022(14), 1717\u20131722 (2006)","journal-title":"Bioinformatics"},{"key":"54_CR6","unstructured":"Dehzangi, A., Amnuaisuk, S.P., Ng, K.H.: Investigating the Influence of Combined Features to Classifiers\u2019 Performance: A Comparison Study on a Protein Fold Prediction Problem. In: 6th IEEE International Conference on Information Technology in Asia, pp. 213\u2013217 (2009)"},{"key":"54_CR7","doi-asserted-by":"crossref","unstructured":"Hashemi, H.B., Shakery, A., Naeini, M.P.: Protein Fold Pattern Recognition Using Bayesian Ensemble of RBF Neural Networks. In: International Conference of Soft Computing and Pattern Recognition SOCPAR, pp. 436\u2013441 (2009)","DOI":"10.1109\/SoCPaR.2009.91"},{"key":"54_CR8","doi-asserted-by":"crossref","unstructured":"Kecman, V., Yang, T.: Protein Fold Recognition with Adaptive Local Hyper plane Algorithm. In: 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology, Nashville, Tennessee, USA, pp. 75\u201378 (2009)","DOI":"10.1109\/CIBCB.2009.4925710"},{"key":"54_CR9","doi-asserted-by":"crossref","unstructured":"Nanni, L.: Ensemble of classifiers for protein fold recognition. In: New Issues in Neurocomputing: 13th European Symposium on Artificial Neural Networks, pp. 850\u2013853 (2006)","DOI":"10.1016\/j.neucom.2005.08.006"},{"key":"54_CR10","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, X., Yang, M.Q., Yang, J.Y.: Ensemble of Probabilistic Neural Networks for Protein Fold Recognition. In: 7th IEEE International Conference on Bioinformatics and Bioengineering, pp. 66\u201370 (2007)","DOI":"10.1109\/BIBE.2007.4375546"},{"key":"54_CR11","doi-asserted-by":"crossref","unstructured":"Dehzangi, A., Amnuaisuk, S.P., Dehzangi, O.: Using Random Forest for Protein Fold Prediction Problem: An Empirical Study. Journal of Information Science and Engineering\u00a026(6) (2010)","DOI":"10.1007\/978-3-642-12211-8_19"},{"key":"54_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/978-3-642-12211-8_19","volume-title":"Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics","author":"A. Dehzangi","year":"2010","unstructured":"Dehzangi, A., Amnuaisuk, S.P., Manafi, M., Safa, S.: Using rotation forest for protein fold prediction problem: An empirical study. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2010. LNCS, vol.\u00a06023, pp. 217\u2013227. Springer, Heidelberg (2010)"},{"key":"54_CR13","doi-asserted-by":"crossref","unstructured":"Krishnaraj, Y., Reddy, C.K.: Boosting methods for Protein Fold Recognition: An Empirical Comparison. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 393\u2013396 (2008)","DOI":"10.1109\/BIBM.2008.83"},{"issue":"10","key":"54_CR14","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1016\/j.compbiomed.2009.07.007","volume":"39","author":"C. Lampros","year":"2009","unstructured":"Lampros, C., Papaloukas, C., Exarchos, K., Fotiadis, D.I., Tsalikakis, D.: Improving the protein fold recognition accuracy of a reduced state-space hidden Markov model. Computers in Biology and Medicine\u00a039(10), 907\u2013914 (2009)","journal-title":"Computers in Biology and Medicine"},{"key":"54_CR15","doi-asserted-by":"crossref","unstructured":"Bologna, G., Appel, R.D.: A comparison study on protein fold recognition. In: Proceedings of the Ninth International Conference on Neural Information Processing, pp. 2492\u20132496 (2002)","DOI":"10.1109\/ICONIP.2002.1201943"},{"issue":"4","key":"54_CR16","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1093\/bioinformatics\/17.4.349","volume":"17","author":"C. Ding","year":"2001","unstructured":"Ding, C., Dubchak, I.: Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics\u00a017(4), 349\u2013358 (2001)","journal-title":"Bioinformatics"},{"key":"54_CR17","unstructured":"Dubchak, I., Muchnik, I., Kim, S.K.: Protein folding class predictor for SCOP: approach based on global descriptors. In: Proceedings in the 5th International Conference on Intelligent Systems for Molecular Biology, pp. 104\u2013107 (1997)"},{"key":"54_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/3-540-45014-9_1","volume-title":"Multiple Classifier Systems","author":"T.G. Dietterich","year":"2000","unstructured":"Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol.\u00a01857, pp. 1\u201315. Springer, Heidelberg (2000)"},{"issue":"1","key":"54_CR19","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L. Breiman","year":"2001","unstructured":"Breiman, L.: Random Forest. Machine learning\u00a045(1), 5\u201332 (2001)","journal-title":"Machine learning"},{"key":"54_CR20","first-page":"123","volume":"24","author":"L. Breiman","year":"1996","unstructured":"Breiman, L.: Bagging Predictors. Machine Learning\u00a024, 123\u2013140 (1996)","journal-title":"Machine Learning"},{"key":"54_CR21","unstructured":"Livingston, F.: Implementation of Breiman\u2019s Random Forest Machine Learning Algorithm, Machine Learning. ECE591Q (2005)"},{"key":"54_CR22","doi-asserted-by":"crossref","unstructured":"Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations\u00a011(1) (2009)","DOI":"10.1145\/1656274.1656278"},{"issue":"10","key":"54_CR23","first-page":"1619","volume":"28","author":"J.J. Rodr\u00edguez","year":"2006","unstructured":"Rodr\u00edguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: A new classifier ensemble method. IEEE Transactions\u00a028(10), 1619\u20131630 (2006)","journal-title":"IEEE Transactions"},{"key":"54_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/978-3-540-72523-7_46","volume-title":"Multiple Classifier Systems","author":"L.I. Kuncheva","year":"2007","unstructured":"Kuncheva, L.I., Rodr\u00edguez, J.J.: An experimental study on rotation forest ensembles. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol.\u00a04472, pp. 459\u2013468. Springer, Heidelberg (2007)"},{"key":"54_CR25","volume-title":"C4.5: Programs for Machine Learning","author":"J.R. Quinlan","year":"1993","unstructured":"Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)"},{"key":"54_CR26","volume-title":"Pattern classification","author":"R.O. Duda","year":"2001","unstructured":"Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification. Wiley, New York (2001)"},{"issue":"2","key":"54_CR27","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1214\/aos\/1016218223","volume":"28","author":"J. Friedman","year":"2000","unstructured":"Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Annals of Statistics\u00a028(2), 337\u2013407 (2000)","journal-title":"Annals of Statistics"},{"issue":"5","key":"54_CR28","first-page":"771","volume":"14","author":"Y. Freund","year":"1999","unstructured":"Freund, Y., Schapier, R.E.: A Short Introduction to Boosting. Journal of Japanese Society for Artificial Intelligence\u00a014(5), 771\u2013780 (1999)","journal-title":"Journal of Japanese Society for Artificial Intelligence"},{"key":"54_CR29","first-page":"536","volume":"247","author":"A.G. Murzin","year":"1995","unstructured":"Murzin, A.G., Brenner, S.E., Hubbard, T., Chothia, C.: SCOP: a structural classification of proteins database for the investigation of sequences and structures. Journal of Molecular Biology\u00a0247, 536\u2013540 (1995)","journal-title":"Journal of Molecular Biology"},{"key":"54_CR30","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1023\/A:1007515423169","volume":"36","author":"E. Bauer","year":"1999","unstructured":"Bauer, E., Kohavi, R.: An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning\u00a036, 105\u2013139 (1999)","journal-title":"Machine Learning"},{"key":"54_CR31","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1093\/bioinformatics\/btn112","volume":"24","author":"T. Damoulas","year":"2008","unstructured":"Damoulas, T., Girolami, M.A.: Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection. Bioinformatics\u00a024, 1264\u20131270 (2008)","journal-title":"Bioinformatics"},{"key":"54_CR32","doi-asserted-by":"crossref","unstructured":"Bouchaffra, D., Tan, J.: Protein Fold Recognition using a Structural Hidden Markov Model. In: 18th International Conference on Pattern Recognition, pp. 186\u2013189 (2006)","DOI":"10.1109\/ICPR.2006.949"},{"issue":"24","key":"54_CR33","doi-asserted-by":"publisher","first-page":"3320","DOI":"10.1093\/bioinformatics\/btm527","volume":"23","author":"M.T.A. Shamim","year":"2007","unstructured":"Shamim, M.T.A., Anwaruddin, M., Nagarajaram, H.A.: Support Vector Machine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs. Bioinformatics\u00a023(24), 3320\u20133327 (2007)","journal-title":"Bioinformatics"}],"container-title":["Lecture Notes in Computer Science","Intelligent Information and Database Systems"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-20039-7_54","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T03:38:21Z","timestamp":1741145901000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-642-20039-7_54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011]]},"ISBN":["9783642200380","9783642200397"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-20039-7_54","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2011]]}}}