{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T15:50:10Z","timestamp":1743004210958,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030614003"},{"type":"electronic","value":"9783030614010"}],"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"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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\/978-3-030-61401-0_55","type":"book-chapter","created":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T09:03:04Z","timestamp":1603184584000},"page":"589-598","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["The Utilization of Different Classifiers to Perform Drug Repositioning in Inclusion Body Myositis Supports the Concept of Biological Invariance"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2244-5161","authenticated-orcid":false,"given":"\u00d3scar","family":"\u00c1lvarez-Machancoses","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8555-3832","authenticated-orcid":false,"given":"Enrique","family":"deAndr\u00e9s-Galiana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4758-2832","authenticated-orcid":false,"given":"Juan Luis","family":"Fern\u00e1ndez-Mart\u00ednez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1002-5095","authenticated-orcid":false,"given":"Andrzej","family":"Kloczkowski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,7]]},"reference":[{"issue":"21","key":"55_CR1","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1056\/NEJM199111213252107","volume":"325","author":"MC Dalakas","year":"1991","unstructured":"Dalakas, M.C.: Polymyositis, dermatomyositis, and inclusion-body myositis. New Engl. J. Med. 325(21), 1487\u20131498 (1991)","journal-title":"New Engl. J. Med."},{"issue":"5","key":"55_CR2","first-page":"705","volume":"38","author":"RC Griggs","year":"1995","unstructured":"Griggs, R.C., et al.: Inclusion body myositis and myopathies. Ann. Neurol. Official J. Am. Neurol. Assoc. Child Neurol. Soc. 38(5), 705\u2013713 (1995)","journal-title":"Ann. Neurol. Official J. Am. Neurol. Assoc. Child Neurol. Soc."},{"issue":"8","key":"55_CR3","doi-asserted-by":"crossref","first-page":"e104048","DOI":"10.1371\/journal.pone.0104048","volume":"9","author":"K Ghannam","year":"2014","unstructured":"Ghannam, K., et al.: Upregulation of immunoproteasome subunits in myositis indicates active inflammation with involvement of antigen presenting cells, CD8 T-cells and IFN\u03b3. PLoS One 9(8), e104048 (2014)","journal-title":"PLoS One"},{"issue":"12","key":"55_CR4","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1016\/j.nmd.2013.08.007","volume":"23","author":"MR Rose","year":"2013","unstructured":"Rose, M.R.: 188th ENMC international workshop: inclusion body myositis, 2\u20134 December 2011, Naarden the Netherlands. Neuromusc. Disord. 23(12), 1044\u20131055 (2013)","journal-title":"Neuromusc. Disord."},{"issue":"Suppl 3","key":"55_CR5","doi-asserted-by":"crossref","first-page":"A164","DOI":"10.1136\/annrheumdis-2013-eular.527","volume":"72","author":"P Machado","year":"2013","unstructured":"Machado, P., et al.: Lb0002 safety and tolerability of arimoclomol in patients with sporadic inclusion body myositis: a randomized, double-blind, placebo controlled, phase IIa proof-of-concept trial. Ann. Rheum. Dis. 72(Suppl 3), A164\u2013A164 (2013)","journal-title":"Ann. Rheum. Dis."},{"key":"55_CR6","doi-asserted-by":"crossref","unstructured":"Gualano, B., et al.: Resistance training with vascular occlusion in inclusion body myositis: a case study. Med. Sci. Sports Exerc. 42(2), 250\u2013254 (2010)","DOI":"10.1249\/MSS.0b013e3181b18fb8"},{"issue":"11","key":"55_CR7","doi-asserted-by":"crossref","first-page":"e74450","DOI":"10.1371\/journal.pone.0074450","volume":"8","author":"N Prevel","year":"2013","unstructured":"Prevel, N., Allenbach, Y., Klatzmann, D., Salomon, B., Benveniste, O.: Beneficial role of rapamycin in experimental autoimmune myositis. PLoS One 8(11), e74450 (2013)","journal-title":"PLoS One"},{"issue":"4","key":"55_CR8","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1016\/j.ymthe.2017.02.015","volume":"25","author":"JR Mendell","year":"2017","unstructured":"Mendell, J.R., et al.: Follistatin gene therapy for sporadic inclusion body myositis improves functional outcomes. Mol. Ther. 25(4), 870\u2013879 (2017)","journal-title":"Mol. Ther."},{"key":"55_CR9","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.jhealeco.2016.01.012","volume":"47","author":"JA DiMasi","year":"2016","unstructured":"DiMasi, J.A., Grabowski, H.G., Hansen, R.W.: Innovation in the pharmaceutical industry: new estimates of R&D costs. J. Health Econ. 47, 20\u201333 (2016)","journal-title":"J. Health Econ."},{"issue":"6","key":"55_CR10","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1038\/nrd4309","volume":"13","author":"D Cook","year":"2014","unstructured":"Cook, D., et al.: Lessons learned from the fate of AstraZeneca\u2019s drug pipeline: a five-dimensional framework. Nature Rev. Drug Discov. 13(6), 419 (2014)","journal-title":"Nature Rev. Drug Discov."},{"issue":"3","key":"55_CR11","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1038\/nrd3681","volume":"11","author":"JW Scannell","year":"2012","unstructured":"Scannell, J.W., Blanckley, A., Boldon, H., Warrington, B.: Diagnosing the decline in pharmaceutical R&D efficiency. Nature Rev. Drug Discov. 11(3), 191 (2012)","journal-title":"Nature Rev. Drug Discov."},{"issue":"8","key":"55_CR12","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1080\/17460441.2019.1621284","volume":"14","author":"\u00d3 \u00c1lvarez-Machancoses","year":"2019","unstructured":"\u00c1lvarez-Machancoses, \u00d3., Fern\u00e1ndez-Mart\u00ednez, J.L.: Using artificial intelligence methods to speed up drug discovery. Expert Opin. Drug Discov. 14(8), 769\u2013777 (2019)","journal-title":"Expert Opin. Drug Discov."},{"issue":"8","key":"55_CR13","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1089\/cmb.2016.0008","volume":"23","author":"EJ de Andr\u00e9s-Galiana","year":"2016","unstructured":"de Andr\u00e9s-Galiana, E.J., Fern\u00e1ndez-Mart\u00ednez, J.L., Sonis, S.T.: Design of biomedical robots for phenotype prediction problems. J. Comput. Biol. 23(8), 678\u2013692 (2016)","journal-title":"J. Comput. Biol."},{"key":"55_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/978-3-319-78759-6_2","volume-title":"Bioinformatics and Biomedical Engineering","author":"A Cernea","year":"2018","unstructured":"Cernea, A., et al.: Sampling defective pathways in phenotype prediction problems via the Fisher\u2019s ratio sampler. In: Rojas, I., Ortu\u00f1o, F. (eds.) IWBBIO 2018. LNCS, vol. 10814, pp. 15\u201323. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-78759-6_2"},{"issue":"1","key":"55_CR15","doi-asserted-by":"crossref","first-page":"W1","DOI":"10.1190\/geo2011-0341.1","volume":"77","author":"JL Fern\u00e1ndez-Mart\u00ednez","year":"2012","unstructured":"Fern\u00e1ndez-Mart\u00ednez, J.L., Fern\u00e1ndez-Mu\u00f1oz, Z., Tompkins, M.J.: On the topography of the cost functional in linear and nonlinear inverse problems. Geophysics 77(1), W1\u2013W15 (2012)","journal-title":"Geophysics"},{"key":"55_CR16","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.jappgeo.2013.07.005","volume":"98","author":"JL Fern\u00e1ndez-Mart\u00ednez","year":"2013","unstructured":"Fern\u00e1ndez-Mart\u00ednez, J.L., Fern\u00e1ndez-Mu\u00f1oz, Z., Pallero, J.L.G., Pedruelo-Gonz\u00e1lez, L.M.: From Bayes to Tarantola: new insights to understand uncertainty in inverse problems. J. Appl. Geophys. 98, 62\u201372 (2013)","journal-title":"J. Appl. Geophys."},{"key":"55_CR17","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.jbi.2016.10.012","volume":"64","author":"EJ de Andr\u00e9s-Galiana","year":"2016","unstructured":"de Andr\u00e9s-Galiana, E.J., Fern\u00e1ndez-Mart\u00ednez, J.L., Sonis, S.T.: Sensitivity analysis of gene ranking methods in phenotype prediction. J. Biomed. Inform. 64, 255\u2013264 (2016)","journal-title":"J. Biomed. Inform."},{"key":"55_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/978-3-319-78759-6_3","volume-title":"Bioinformatics and Biomedical Engineering","author":"JL Fern\u00e1ndez-Mart\u00ednez","year":"2018","unstructured":"Fern\u00e1ndez-Mart\u00ednez, J.L., et al.: Sampling defective pathways in phenotype prediction problems via the holdout sampler. In: Rojas, I., Ortu\u00f1o, F. (eds.) IWBBIO 2018. LNCS, vol. 10814, pp. 24\u201332. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-78759-6_3"},{"key":"55_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/978-3-319-78759-6_4","volume-title":"Bioinformatics and Biomedical Engineering","author":"A Cernea","year":"2018","unstructured":"Cernea, A., et al.: Comparison of different sampling algorithms for phenotype prediction. In: Rojas, I., Ortu\u00f1o, F. (eds.) IWBBIO 2018. LNCS, vol. 10814, pp. 33\u201345. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-78759-6_4"},{"key":"55_CR20","doi-asserted-by":"crossref","unstructured":"Saligan, L.N., Fern\u00e1ndez-Mart\u00ednez, J.L., de Andr\u00e9s-Galiana, E.J., Sonis, S.: Supervised classification by filter methods and recursive feature elimination predicts risk of radiotherapy-related fatigue in patients with prostate cancer. Cancer Inform. 13, CIN-S19745 (2014)","DOI":"10.4137\/CIN.S19745"},{"issue":"3","key":"55_CR21","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","volume":"46","author":"NS Altman","year":"1992","unstructured":"Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175\u2013185 (1992)","journal-title":"Am. Stat."},{"issue":"1\u20133","key":"55_CR22","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G-B Huang","year":"2006","unstructured":"Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133), 489\u2013501 (2006)","journal-title":"Neurocomputing"},{"issue":"1","key":"55_CR23","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"issue":"1","key":"55_CR24","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/nrc2044","volume":"7","author":"J Lamb","year":"2007","unstructured":"Lamb, J.: The connectivity map: a new tool for biomedical research. Nat. Rev. Cancer 7(1), 54 (2007)","journal-title":"Nat. Rev. Cancer"},{"issue":"8","key":"55_CR25","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1212\/WNL.59.8.1170","volume":"59","author":"SA Greenberg","year":"2002","unstructured":"Greenberg, S.A.: Molecular profiles of inflammatory myopathies. Neurology 59(8), 1170\u20131182 (2002)","journal-title":"Neurology"},{"issue":"21","key":"55_CR26","doi-asserted-by":"crossref","first-page":"2008","DOI":"10.1212\/01.WNL.0000291619.17160.b8","volume":"69","author":"SA Greenberg","year":"2007","unstructured":"Greenberg, S.A.: Proposed immunologic models of the inflammatory myopathies and potential therapeutic implications. Neurology 69(21), 2008\u20132019 (2007)","journal-title":"Neurology"},{"issue":"16","key":"55_CR27","doi-asserted-by":"crossref","first-page":"2028","DOI":"10.1093\/bioinformatics\/btl344","volume":"22","author":"H Pang","year":"2006","unstructured":"Pang, H., et al.: Pathway analysis using random forests classification and regression. Bioinformatics 22(16), 2028\u20132036 (2006)","journal-title":"Bioinformatics"},{"issue":"2","key":"55_CR28","doi-asserted-by":"crossref","first-page":"7","DOI":"10.29245\/2572-9411\/2019\/2.1174","volume":"4","author":"JL Fern\u00e1ndez-Mart\u00ednez","year":"2019","unstructured":"Fern\u00e1ndez-Mart\u00ednez, J.L., \u00c1lvarez, \u00d3., de Andr\u00e9s-Galiana, E.J., de la Vi\u00f1a, J.F.S., Huergo, L.: Robust sampling of altered pathways for drug repositioning reveals promising novel therapeutics for inclusion body myositis. J Rare Dis. Res. Treat 4(2), 7\u201315 (2019)","journal-title":"J Rare Dis. Res. Treat"},{"issue":"1","key":"55_CR29","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1111\/j.1749-6632.2002.tb04306.x","volume":"967","author":"M K\u00fcrthy","year":"2002","unstructured":"K\u00fcrthy, M., et al.: Effect of BRX-220 against peripheral neuropathy and insulin resistance in diabetic rat models. Ann. New York Acad. Sci. 967(1), 482\u2013489 (2002)","journal-title":"Ann. New York Acad. Sci."},{"issue":"7216","key":"55_CR30","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1016\/s0140-6736(61)90927-8","volume":"278","author":"WG McBride","year":"1961","unstructured":"McBride, W.G.: Thalidomide and congenital abnormalities. Lancet 278(7216), 1358 (1961). https:\/\/doi.org\/10.1016\/s0140-6736(61)90927-8","journal-title":"Lancet"},{"issue":"1","key":"55_CR31","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1001\/archderm.142.1.70","volume":"142","author":"D Sereda","year":"2006","unstructured":"Sereda, D., Werth, V.P.: Improvement in dermatomyositis rash associated with the use of antiestrogen medication. Arch. Dermatol. 142(1), 70\u201372 (2006)","journal-title":"Arch. Dermatol."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61401-0_55","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T04:00:10Z","timestamp":1723780810000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-61401-0_55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030614003","9783030614010"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61401-0_55","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":"7 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAISC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence and Soft Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zakopane","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icaisc2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icaisc.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}