{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T21:04:41Z","timestamp":1743023081956,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030881627"},{"type":"electronic","value":"9783030881634"}],"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\/978-3-030-88163-4_29","type":"book-chapter","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T04:08:05Z","timestamp":1633838885000},"page":"339-349","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparison of Fusion Methodologies Using CNV and RNA-Seq for Cancer Classification: A Case Study on Non-Small-Cell Lung Cancer"],"prefix":"10.1007","author":[{"given":"Francisco","family":"Carrillo-Perez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan Carlos","family":"Morales","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Castillo-Secilla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alberto","family":"Guillen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ignacio","family":"Rojas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luis Javier","family":"Herrera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,9]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Castillo, D., et al.: Leukemia multiclass assessment and classification from microarray and rna-seq technologies integration at gene expression level. PloS One 14(2), e0212127 (2019)","key":"29_CR1","DOI":"10.1371\/journal.pone.0212127"},{"issue":"1","key":"29_CR2","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1186\/s12859-017-1925-0","volume":"18","author":"D Castillo","year":"2017","unstructured":"Castillo, D., G\u00e1lvez, J.M., Herrera, L.J., San Rom\u00e1n, B., Rojas, F., Rojas, I.: Integration of rna-seq data with heterogeneous microarray data for breast cancer profiling. BMC Bioinf. 18(1), 506 (2017)","journal-title":"BMC Bioinf."},{"doi-asserted-by":"crossref","unstructured":"Castillo-Secilla, D., et al.: Knowseq r-bioc package: the automatic smart gene expression tool for retrieving relevant biological knowledge. Comput. Biol. Med. 133, 104387 (2021)","key":"29_CR3","DOI":"10.1016\/j.compbiomed.2021.104387"},{"doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","key":"29_CR4","DOI":"10.1145\/2939672.2939785"},{"issue":"02","key":"29_CR5","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1142\/S0219720005001004","volume":"3","author":"C Ding","year":"2005","unstructured":"Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinf. Comput. Biol. 3(02), 185\u2013205 (2005)","journal-title":"J. Bioinf. Comput. Biol."},{"doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Mlw-gcforest: a multi-weighted gcforest model towards the staging of lung adenocarcinoma based on multi-modal genetic data. BMC Bioinf. 20(1), 1\u201314 (2019)","key":"29_CR6","DOI":"10.1186\/s12859-019-3172-z"},{"doi-asserted-by":"crossref","unstructured":"G\u00e1lvez, J.M., et al.: Towards improving skin cancer diagnosis by integrating microarray and rna-seq datasets. IEEE J. Biomed. Health Inf. 24(7), 2119\u20132130 (2019)","key":"29_CR7","DOI":"10.1109\/JBHI.2019.2953978"},{"unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256. JMLR Workshop and Conference Proceedings (2010)","key":"29_CR8"},{"doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez, S., Castillo, D., Galvez, J.M., Rojas, I., Herrera, L.J.: Feature selection and assessment of lung cancer sub-types by applying predictive models. In: International Work-Conference on Artificial Neural Networks, pp. 883\u2013894. Springer (2019)","key":"29_CR9","DOI":"10.1007\/978-3-030-20518-8_73"},{"doi-asserted-by":"crossref","unstructured":"Grossman, R.L., et al.: Toward a shared vision for cancer genomic data. New England J. Med. 375(12), 1109\u20131112 (2016)","key":"29_CR10","DOI":"10.1056\/NEJMp1607591"},{"doi-asserted-by":"crossref","unstructured":"Hanna, N., et al.: Systemic therapy for stage iv non-small-cell lung cancer: american society of clinical oncology clinical practice guideline update. J. Clin. Oncol. (2017)","key":"29_CR11","DOI":"10.1200\/JOP.2017.026716"},{"issue":"1","key":"29_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-019-0211-0","volume":"3","author":"SC Huang","year":"2020","unstructured":"Huang, S.C., Pareek, A., Seyyedi, S., Banerjee, I., Lungren, M.P.: Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. NPJ Digital Med. 3(1), 1\u20139 (2020)","journal-title":"NPJ Digital Med."},{"doi-asserted-by":"crossref","unstructured":"Kenfield, S.A., Wei, E.K., Stampfer, M.J., Rosner, B.A., Colditz, G.A.: Comparison of aspects of smoking among the four histological types of lung cancer. Tobacco Control 17(3), 198\u2013204 (2008)","key":"29_CR13","DOI":"10.1136\/tc.2007.022582"},{"unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)","key":"29_CR14"},{"doi-asserted-by":"crossref","unstructured":"Lawrence, M., et al.: Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9(8), e1003118 (2013)","key":"29_CR15","DOI":"10.1371\/journal.pcbi.1003118"},{"key":"29_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2020.107277","volume":"87","author":"TY Lee","year":"2020","unstructured":"Lee, T.Y., Huang, K.Y., Chuang, C.H., Lee, C.Y., Chang, T.H.: Incorporating deep learning and multi-omics autoencoding for analysis of lung adenocarcinoma prognostication. Comput. Biol. Chem. 87, 107277 (2020)","journal-title":"Comput. Biol. Chem."},{"unstructured":"Paszke, A., et al.: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019 Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024\u20138035. Curran Associates, Inc. (2019), http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf","key":"29_CR17"},{"key":"29_CR18","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"8","key":"29_CR19","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Patt Anal. Mach. Intell. 27(8), 1226\u20131238 (2005)","journal-title":"IEEE Trans. Patt Anal. Mach. Intell."},{"unstructured":"Portal, G.: Gdc rna-seq analysis pipeline. https:\/\/docs.gdc.cancer.gov\/Data\/Bioinformatics_Pipelines\/Expression_mRNA_Pipeline\/. Accessed 4 Jul 2020","key":"29_CR20"},{"issue":"7","key":"29_CR21","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1002\/gcc.22460","volume":"56","author":"ZW Qiu","year":"2017","unstructured":"Qiu, Z.W., Bi, J.H., Gazdar, A.F., Song, K.: Genome-wide copy number variation pattern analysis and a classification signature for non-small cell lung cancer. Genes Chromosom. Cancer 56(7), 559\u2013569 (2017)","journal-title":"Genes Chromosom. Cancer"},{"doi-asserted-by":"crossref","unstructured":"Ritchie, M.E., et al.: Limma powers differential expression analyses for rna-sequencing and microarray studies. Nucleic Acids Res. 43(7), e47\u2013e47 (2015)","key":"29_CR22","DOI":"10.1093\/nar\/gkv007"},{"doi-asserted-by":"crossref","unstructured":"Ross, D.T., et al.: Systematic variation in gene expression patterns in human cancer cell lines. Nat. Genet. 24(3), 227\u2013235 (2000)","key":"29_CR23","DOI":"10.1038\/73432"},{"issue":"6","key":"29_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/gm62","volume":"1","author":"A Shlien","year":"2009","unstructured":"Shlien, A., Malkin, D.: Copy number variations and cancer. Genome Med. 1(6), 1\u20139 (2009)","journal-title":"Genome Med."},{"doi-asserted-by":"crossref","unstructured":"Snoek, C.G., Worring, M., Smeulders, A.W.: Early versus late fusion in semantic video analysis. In: Proceedings of the 13th Annual ACM International Conference on Multimedia, pp. 399\u2013402 (2005)","key":"29_CR25","DOI":"10.1145\/1101149.1101236"},{"issue":"5","key":"29_CR26","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1200\/JCO.2006.06.8015","volume":"25","author":"J Subramanian","year":"2007","unstructured":"Subramanian, J., Govindan, R.: Lung cancer in never smokers: a review. J. Clin. Oncol. 25(5), 561\u2013570 (2007)","journal-title":"J. Clin. Oncol."},{"doi-asserted-by":"crossref","unstructured":"Sung, H., et al.: Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer J. Clin. 71(3), pp. 209-249 (2021)","key":"29_CR27","DOI":"10.3322\/caac.21660"},{"doi-asserted-by":"publisher","unstructured":"Heigener, D.F., Reck, M.: Der Internist 58(12), 1258\u20131263 (2017). https:\/\/doi.org\/10.1007\/s00108-017-0339-4","key":"29_CR28","DOI":"10.1007\/s00108-017-0339-4"},{"unstructured":"UK, C.R.: Types of lung cancer. https:\/\/www.cancerresearchuk.org\/about-cancer\/lung-cancer\/stages-types-grades\/types","key":"29_CR29"},{"issue":"10","key":"29_CR30","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/ng.2764","volume":"45","author":"JN Weinstein","year":"2013","unstructured":"Weinstein, J.N., et al.: The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45(10), 1113 (2013)","journal-title":"Nat. Genet."}],"container-title":["Lecture Notes in Computer Science","Bioengineering and Biomedical Signal and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88163-4_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T04:18:38Z","timestamp":1633839518000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88163-4_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030881627","9783030881634"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88163-4_29","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":"9 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BIOMESIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Bioengineering and Biomedical Signal and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Meloneras","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"biomesip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/biomesip.ugr.es\/index.html","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":"121","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":"41","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":"5","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":"34% - 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":"3.1","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":"2.1","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)"}}]}}