{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:22:20Z","timestamp":1742912540250,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030916985"},{"type":"electronic","value":"9783030916992"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-91699-2_6","type":"book-chapter","created":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T05:03:29Z","timestamp":1637989409000},"page":"73-88","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ensemble of Protein Stability upon Point Mutation Predictors"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1476-4779","authenticated-orcid":false,"given":"Eduardo Kenji Hasegawa","family":"de Freitas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6377-9379","authenticated-orcid":false,"given":"Alex Dias","family":"Camargo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7653-0962","authenticated-orcid":false,"given":"Maur\u00edcio","family":"Balboni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7107-5024","authenticated-orcid":false,"given":"Adriano V.","family":"Werhli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8966-5708","authenticated-orcid":false,"given":"Karina","family":"dos Santos Machado","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,28]]},"reference":[{"issue":"2","key":"6_CR1","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1002\/humu.20280","volume":"27","author":"J Auclair","year":"2006","unstructured":"Auclair, J., et al.: Systematic mRNA analysis for the effect of MLH1 and MSH2 missense and silent mutations on aberrant splicing. Hum. Mutat. 27(2), 145\u2013154 (2006). https:\/\/doi.org\/10.1002\/humu.20280","journal-title":"Hum. Mutat."},{"key":"6_CR2","doi-asserted-by":"publisher","unstructured":"Bava, K.A., Gromiha, M.M., Uedaira, H., Kitajima, K., Sarai, A.: ProTherm, version 4.0: thermodynamic database for proteins and mutants. Nucleic Acids Res. 32(1), 120\u2013121 (01 2004). https:\/\/doi.org\/10.1093\/nar\/gkh082","DOI":"10.1093\/nar\/gkh082"},{"issue":"2","key":"6_CR3","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123\u2013140 (1996)","journal-title":"Mach. Learn."},{"issue":"Suppl 1","key":"6_CR4","doi-asserted-by":"publisher","first-page":"i63","DOI":"10.1093\/bioinformatics\/bth928","volume":"20","author":"E Capriotti","year":"2004","unstructured":"Capriotti, E., Fariselli, P., Casadio, R.: A neural-network-based method for predicting protein stability changes upon single point mutations. Bioinformatics 20(Suppl 1), i63\u2013i68 (2004). https:\/\/doi.org\/10.1093\/bioinformatics\/bth928","journal-title":"Bioinformatics"},{"key":"6_CR5","doi-asserted-by":"publisher","unstructured":"Capriotti, E., Fariselli, P., Rossi, I., Casadio, R.: A three-state prediction of single point mutations on protein stability changes. BMC Bioinform. 9(Suppl 2) (2008). https:\/\/doi.org\/10.1186\/1471-2105-9-s2-s6","DOI":"10.1186\/1471-2105-9-s2-s6"},{"key":"6_CR6","doi-asserted-by":"publisher","unstructured":"Cheng, J., Randall, A., Baldi, P.: Prediction of protein stability changes for single-site mutations using support vector machines. Prot. Struct. Function Bioinform. 62(4), 1125\u20131132 (2005). https:\/\/doi.org\/10.1002\/prot.20810","DOI":"10.1002\/prot.20810"},{"key":"6_CR7","doi-asserted-by":"publisher","unstructured":"Dehouck, Y., Kwasigroch, J.M., Gilis, D., Rooman, M.: PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality. BMC Bioinform. 12(1) (2011). https:\/\/doi.org\/10.1186\/1471-2105-12-151","DOI":"10.1186\/1471-2105-12-151"},{"issue":"2","key":"6_CR8","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s11704-019-8208-z","volume":"14","author":"X Dong","year":"2020","unstructured":"Dong, X., Yu, Z., Cao, W., Shi, Y., Ma, Q.: A survey on ensemble learning. Front. Comput. Sci. 14(2), 241\u2013258 (2020)","journal-title":"Front. Comput. Sci."},{"issue":"1","key":"6_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-14-88","volume":"14","author":"J Eickholt","year":"2013","unstructured":"Eickholt, J., Cheng, J.: DNdisorder: predicting protein disorder using boosting and deep networks. BMC Bioinform. 14(1), 1 (2013)","journal-title":"BMC Bioinform."},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Fersht, A.R.: Protein folding and stability: the pathway of folding of Barnase. FEBS Lett. 325(1\u20132), 5\u201316 (1993)","DOI":"10.1016\/0014-5793(93)81405-O"},{"key":"6_CR11","unstructured":"Freund, Y., Schapire, R.E., et al.: Experiments with a new boosting algorithm. In: ICML, vol. 96, pp. 148\u2013156 (1996)"},{"issue":"2","key":"6_CR12","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/s0022-2836(02)00442-4","volume":"320","author":"R Guerois","year":"2002","unstructured":"Guerois, R., Nielsen, J.E., Serrano, L.: Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J. Mol. Biol. 320(2), 369\u2013387 (2002). https:\/\/doi.org\/10.1016\/s0022-2836(02)00442-4","journal-title":"J. Mol. Biol."},{"key":"6_CR13","volume-title":"Data Mining - Concepts and Techniques","author":"J Han","year":"2006","unstructured":"Han, J., Pei, J., Kamber, M.: Data Mining - Concepts and Techniques. Morgan and Kaufmann, San Francisco (2006)"},{"key":"6_CR14","doi-asserted-by":"publisher","unstructured":"Laimer, J., Hofer, H., Fritz, M., Wegenkittl, S., Lackner, P.: Maestro - multi agent stability prediction upon point mutations. BMC Bioinform. 16(1) (2015). https:\/\/doi.org\/10.1186\/s12859-015-0548-6","DOI":"10.1186\/s12859-015-0548-6"},{"key":"6_CR15","unstructured":"Mendoza, M.R., Bazzan, A.L.C.: The wisdom of crowds in bioinformatics: what can we learn (and gain) from ensemble predictions? In: Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, pp. 1678\u20131679 (2013)"},{"issue":"2","key":"6_CR16","first-page":"340","volume":"9","author":"H No\u00e7airi","year":"2016","unstructured":"No\u00e7airi, H., Gomes, C., Thomas, M., Saporta, G.: Improving stacking methodology for combining classifiers; applications to cosmetic industry. Electron. J. Appl. Stat. Anal. 9(2), 340\u2013361 (2016)","journal-title":"Electron. J. Appl. Stat. Anal."},{"key":"6_CR17","doi-asserted-by":"publisher","unstructured":"Parthiban, V., Gromiha, M.M., Schomburg, D.: CUPSAT: prediction of protein stability upon point mutations. Nucl. Acids Res. 34(Web Server), January 2006. https:\/\/doi.org\/10.1093\/nar\/gkl190","DOI":"10.1093\/nar\/gkl190"},{"issue":"3","key":"6_CR18","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1093\/bioinformatics\/btt691","volume":"30","author":"DEV Pires","year":"2013","unstructured":"Pires, D.E.V., Ascher, D.B., Blundell, T.L.: mcSM: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics 30(3), 335\u2013342 (2013). https:\/\/doi.org\/10.1093\/bioinformatics\/btt691","journal-title":"Bioinformatics"},{"issue":"W1","key":"6_CR19","doi-asserted-by":"publisher","first-page":"W314","DOI":"10.1093\/nar\/gku411","volume":"42","author":"DE Pires","year":"2014","unstructured":"Pires, D.E., Ascher, D.B., Blundell, T.L.: DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach. Nucl. Acids Res. 42(W1), W314\u2013W319 (2014)","journal-title":"Nucl. Acids Res."},{"issue":"4","key":"6_CR20","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1039\/c3mb70486f","volume":"10","author":"I Saha","year":"2014","unstructured":"Saha, I., Zubek, J., Klingstr\u00f6m, T., Forsberg, S., Wikander, J., Kierczak, M., Maulik, U., Plewczynski, D.: Ensemble learning prediction of protein-protein interactions using proteins functional annotations. Mol. BioSyst. 10(4), 820\u2013830 (2014)","journal-title":"Mol. BioSyst."},{"issue":"5","key":"6_CR21","doi-asserted-by":"publisher","first-page":"2178","DOI":"10.1016\/S0006-3495(98)77661-1","volume":"75","author":"Y Sugita","year":"1998","unstructured":"Sugita, Y., Kitao, A.: Dependence of protein stability on the structure of the denatured state: free energy calculations of I56V mutation in human lysozyme. Biophys. J. 75(5), 2178\u20132187 (1998)","journal-title":"Biophys. J."},{"key":"6_CR22","unstructured":"Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Education, London (2016)"},{"key":"6_CR23","unstructured":"Verli, H.: Bioinform\u00e1tica: da biologia \u00e0 flexibilidade molecular. Sociedade Brasileira de Bioqu\u00edmica e Biologia Molecular (2014)"},{"key":"6_CR24","unstructured":"Witten, I.H., Frank, E., Hall, M.A.: Data Mining Practical Machine Learning Tools and Techniques Third Edition. Morgan Kaufmann, Burlington (2016)"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Worth, C.L., Preissner, R., Blundell, T.L.: SDM-a server for predicting effects of mutations on protein stability and malfunction. Nucl. Acids Res. 39(suppl_2), W215\u2013W222 (2011)","DOI":"10.1093\/nar\/gkr363"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Yang, P., Yang, Y.H., Zhou, B.B., Zomaya, A.Y.: A review of ensemble methods in bioinformatics. Current Bioinform. 5(4), 296\u2013308 (2010)","DOI":"10.2174\/157489310794072508"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Yang, Y.: Temporal Data Mining via Unsupervised Ensemble Learning. Elsevier, Amsterdam (2016)","DOI":"10.1016\/B978-0-12-811654-8.00002-6"},{"issue":"6","key":"6_CR28","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1038\/nmeth0607-466","volume":"4","author":"S Yin","year":"2007","unstructured":"Yin, S., Ding, F., Dokholyan, N.V.: Eris: an automated estimator of protein stability. Nat. Meth. 4(6), 466\u2013467 (2007). https:\/\/doi.org\/10.1038\/nmeth0607-466","journal-title":"Nat. Meth."},{"key":"6_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2012\/805827","volume":"2012","author":"Z Zhang","year":"2012","unstructured":"Zhang, Z., Miteva, M.A., Wang, L., Alexov, E.: Analyzing effects of naturally occurring missense mutations. Comput. Math. Meth. Med. 2012, 1\u201315 (2012). https:\/\/doi.org\/10.1155\/2012\/805827","journal-title":"Comput. Math. Meth. Med."},{"issue":"11","key":"6_CR30","doi-asserted-by":"publisher","first-page":"2714","DOI":"10.1110\/ps.0217002","volume":"11","author":"H Zhou","year":"2009","unstructured":"Zhou, H., Zhou, Y.: Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Prot. Sci. 11(11), 2714\u20132726 (2009). https:\/\/doi.org\/10.1110\/ps.0217002","journal-title":"Prot. Sci."},{"key":"6_CR31","unstructured":"Zhou, Z.H.: Ensemble Methods: Foundations and Algorithms. Chapman and Hall\/CRC, Boca Raton (2019)"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-91699-2_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T05:03:59Z","timestamp":1637989439000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-91699-2_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030916985","9783030916992"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-91699-2_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"28 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/c4ai.inova.usp.br\/bracis\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"JEMS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"192","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"77","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.1","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to COVID-19, the conference was held as an online event.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}