{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T17:32:57Z","timestamp":1748885577233,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030345846"},{"type":"electronic","value":"9783030345853"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-34585-3_23","type":"book-chapter","created":{"date-parts":[[2020,1,22]],"date-time":"2020-01-22T13:02:57Z","timestamp":1579698177000},"page":"262-276","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Analysis of Extremely Obese Individuals Using Deep Learning Stacked Autoencoders and Genome-Wide Genetic Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5690-2474","authenticated-orcid":false,"given":"Casimiro A.","family":"Curbelo Monta\u00f1ez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7070-4447","authenticated-orcid":false,"given":"Paul","family":"Fergus","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0822-1150","authenticated-orcid":false,"given":"Carl","family":"Chalmers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jade","family":"Hind","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,1,23]]},"reference":[{"issue":"S7","key":"23_CR1","doi-asserted-by":"publisher","first-page":"S120","DOI":"10.1038\/ijo.2008.247","volume":"32","author":"WPT James","year":"2008","unstructured":"James, W.P.T.: WHO recognition of the global obesity epidemic. Int. J. Obes. 32(S7), S120\u2013S126 (2008)","journal-title":"Int. J. Obes."},{"issue":"3","key":"23_CR2","doi-asserted-by":"publisher","first-page":"512","DOI":"10.2105\/AJPH.2013.301597","volume":"104","author":"LN Borrell","year":"2014","unstructured":"Borrell, L.N., Samuel, L.: Body mass index categories and mortality risk in US adults: the effect of overweight and obesity on advancing death. Am. J. Public Health 104(3), 512\u2013519 (2014)","journal-title":"Am. J. Public Health"},{"issue":"SUPPL. 2","key":"23_CR3","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1093\/hmg\/ddl215","volume":"15","author":"AJ Walley","year":"2006","unstructured":"Walley, A.J., Blakemore, A.I.F., Froguel, P.: Genetics of obesity and the prediction of risk for health. Hum. Mol. Genet. 15(SUPPL. 2), 124\u2013130 (2006)","journal-title":"Hum. Mol. Genet."},{"issue":"November 2014","key":"23_CR4","first-page":"589","volume":"140","author":"KR Rao","year":"2015","unstructured":"Rao, K.R., Lal, N., Giridharan, N.V.: Genetic & epigenetic approach to human obesity. Indian J. Med. Res. 140(November 2014), 589\u2013603 (2015)","journal-title":"Indian J. Med. Res."},{"key":"23_CR5","volume-title":"Genetic Variation","author":"JM Walker","year":"2010","unstructured":"Walker, J.M.: Genetic Variation, vol. 628. Humana Press, Totowa (2010)"},{"issue":"7538","key":"23_CR6","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1038\/nature14177","volume":"518","author":"AE Locke","year":"2015","unstructured":"Locke, A.E., Kahali, B., Berndt, S.I., Justice, A.E., Pers, T.H., Day, F.R.: Genetic studies of body mass index yield new insights for obesity biology. Nature 518(7538), 197\u2013206 (2015)","journal-title":"Nature"},{"issue":"3","key":"23_CR7","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.ajhg.2009.08.006","volume":"85","author":"JH Moore","year":"2009","unstructured":"Moore, J.H., Williams, S.M.: Epistasis and its implications for personal genetics. Am. J. Hum. Genet. 85(3), 309\u2013320 (2009)","journal-title":"Am. J. Hum. Genet."},{"issue":"301","key":"23_CR8","doi-asserted-by":"publisher","first-page":"S13","DOI":"10.1249\/MSS.0b013e3182399bc8","volume":"44","author":"KY Chen","year":"2012","unstructured":"Chen, K.Y., Janz, K.F., Zhu, W., Brychta, R.J.: Redefining the roles of sensors in objective physical activity monitoring. Med. Sci. Sports Exerc. 44(301), S13\u2013S23 (2012)","journal-title":"Med. Sci. Sports Exerc."},{"issue":"5","key":"23_CR9","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1038\/nrg2344","volume":"9","author":"MI McCarthy","year":"2008","unstructured":"McCarthy, M.I., et al.: Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat. Rev. Genet. 9(5), 356\u2013369 (2008)","journal-title":"Nat. Rev. Genet."},{"issue":"1","key":"23_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbm058","volume":"9","author":"W Li","year":"2007","unstructured":"Li, W.: Three lectures on case control genetic association analysis. Brief. Bioinform. 9(1), 1\u201313 (2007)","journal-title":"Brief. Bioinform."},{"issue":"3","key":"23_CR11","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1086\/519795","volume":"81","author":"S Purcell","year":"2007","unstructured":"Purcell, S., et al.: PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81(3), 559\u2013575 (2007)","journal-title":"Am. J. Hum. Genet."},{"issue":"2","key":"23_CR12","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1038\/nprot.2010.182","volume":"6","author":"GM Clarke","year":"2011","unstructured":"Clarke, G.M., Anderson, C.A., Pettersson, F.H., Cardon, L.R., Morris, A.P., Zondervan, K.T.: Basic statistical analysis in genetic case-control studies. Nat. Protoc. 6(2), 121\u2013133 (2011)","journal-title":"Nat. Protoc."},{"issue":"4","key":"23_CR13","doi-asserted-by":"publisher","first-page":"256","DOI":"10.5808\/GI.2012.10.4.256","volume":"10","author":"S Lee","year":"2012","unstructured":"Lee, S., Kwon, M.-S., Park, T.: Network graph analysis of gene-gene interactions in genome-wide association study data. Genomics Inform. 10(4), 256 (2012)","journal-title":"Genomics Inform."},{"issue":"6","key":"23_CR14","doi-asserted-by":"publisher","first-page":"946","DOI":"10.3906\/sag-1310-77","volume":"44","author":"H G\u00fcl","year":"2014","unstructured":"G\u00fcl, H., Aydin Son, Y., A\u00e7ikel, C.: Discovering missing heritability and early risk prediction for type 2 diabetes: a new perspective for genome-wide association study analysis with the Nurses\u2019 Health Study and the Health Professionals\u2019 Follow-Up Study. Turk. J. Med. Sci. 44(6), 946\u2013954 (2014)","journal-title":"Turk. J. Med. Sci."},{"key":"23_CR15","unstructured":"Ng, A.: Sparse Autoencoder. In: CS294A Lecture Notes, pp. 1\u201319 (2011)"},{"issue":"6088","key":"23_CR16","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533\u2013536 (1986)","journal-title":"Nature"},{"key":"23_CR17","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 315\u2013323 (2011)"},{"key":"23_CR18","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281\u2013305 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"23_CR19","unstructured":"Le, Q.V.: A Tutorial on Deep Learning Part 2: Autoencoders, Convolutional Neural Networks and Recurrent Neural Networks, Mountain View, CA (2015)"},{"key":"23_CR20","first-page":"1","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, p. 1. MIT Press, Cambridge (2016)"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: Advances in Neural Information Processing Systems, pp. 153\u2013160. MIT Press (2007)","DOI":"10.7551\/mitpress\/7503.003.0024"},{"key":"23_CR22","unstructured":"Danaee, P., Ghaeini, R., Hendrix, D.A.: A deep learning approach for cancer detection and relevant gene identification. In: Pacific Symposium on Biocomputing, vol. 22, no. 4, pp. 219\u2013229, January 2017"},{"issue":"11","key":"23_CR23","doi-asserted-by":"publisher","first-page":"e112987","DOI":"10.1371\/journal.pone.0112987","volume":"9","author":"N Salari","year":"2014","unstructured":"Salari, N., Shohaimi, S., Najafi, F., Nallappan, M., Karishnarajah, I.: A novel hybrid classification model of genetic algorithms, modified k-nearest neighbor and developed backpropagation neural network. PLoS One 9(11), e112987 (2014)","journal-title":"PLoS One"},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Fergus, P., Curbelo, C., Abdulaimma, B., Lisboa, P., Chalmers, C., Pineles, B.: Utilising deep learning and genome wide association studies for epistatic-driven preterm birth classification in African-American women. IEEE\/ACM Trans. Comput. Biol. Bioinform. (2018)","DOI":"10.1109\/TCBB.2018.2868667"},{"key":"23_CR25","unstructured":"Curbelo, C., Fergus, P., Curbelo, A., Hussain, A., Al-Jumeily, D., Chalmers, C.: Deep learning classification of polygenic obesity using genome wide association study SNPs. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138 (2018)"},{"issue":"9","key":"23_CR26","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1038\/nmeth.3968","volume":"13","author":"J Lever","year":"2016","unstructured":"Lever, J., Krzywinski, M., Altman, N.: Model selection and overfitting. Nat. Methods 13(9), 703\u2013704 (2016)","journal-title":"Nat. Methods"},{"key":"23_CR27","unstructured":"Zeiler, M.D.: ADADELTA: an adaptive learning rate method. arXiv:1212.5701, December 2012"},{"issue":"2","key":"23_CR28","doi-asserted-by":"publisher","first-page":"80","DOI":"10.4161\/bioe.26997","volume":"5","author":"T Manning","year":"2014","unstructured":"Manning, T., Sleator, R.D., Walsh, P.: Biologically inspired intelligent decision making. Bioengineered 5(2), 80\u201395 (2014)","journal-title":"Bioengineered"},{"key":"23_CR29","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Imieli\u0144ski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of 1993 ACM SIGMOD International Conference on Management of Data, vol. 22, no. 2, pp. 207\u2013216, June 1993","DOI":"10.1145\/170036.170072"}],"container-title":["Lecture Notes in Computer Science","Computational Intelligence Methods for Bioinformatics and Biostatistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-34585-3_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T13:36:30Z","timestamp":1695648990000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-34585-3_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030345846","9783030345853"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-34585-3_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"23 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CIBB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Caparica","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cibb2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eventos.fct.unl.pt\/cibb2018\/pages\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51","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":"32","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":"63% - 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":"2","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":"2","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}