{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:40:39Z","timestamp":1742974839916,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031172656"},{"type":"electronic","value":"9783031172663"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-17266-3_7","type":"book-chapter","created":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T19:35:39Z","timestamp":1663788939000},"page":"68-77","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CanDLE: Illuminating Biases in\u00a0Transcriptomic Pan-Cancer Diagnosis"],"prefix":"10.1007","author":[{"given":"Gabriel","family":"Mej\u00eda","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natasha","family":"Bloch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pablo","family":"Arbelaez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,22]]},"reference":[{"key":"7_CR1","unstructured":"The Cancer Genome Atlas Program - National Cancer Institute. https:\/\/www.cancer.gov\/about-nci\/organization\/ccg\/research\/structural-genomics\/tcga"},{"key":"7_CR2","doi-asserted-by":"publisher","unstructured":"Ahn, T., et al.: Deep learning-based identification of cancer or normal tissue using gene expression data, pp. 1748\u20131752. IEEE (2018). https:\/\/doi.org\/10.1109\/BIBM.2018.8621108","DOI":"10.1109\/BIBM.2018.8621108"},{"issue":"8","key":"7_CR3","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1186\/S12918-018-0642-2","volume":"12","author":"HIH Chen","year":"2018","unstructured":"Chen, H.I.H., Chiu, Y.C., Zhang, T., Zhang, S., Huang, Y., Chen, Y.: GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization. BMC Syst. Biol. 12(8), 45\u201357 (2018). https:\/\/doi.org\/10.1186\/S12918-018-0642-2","journal-title":"BMC Syst. Biol."},{"key":"7_CR4","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1093\/bioinformatics\/bts635","volume":"29","author":"A Dobin","year":"2013","unstructured":"Dobin, A., et al.: STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15\u201321 (2013). https:\/\/doi.org\/10.1093\/bioinformatics\/bts635","journal-title":"Bioinformatics"},{"key":"7_CR5","doi-asserted-by":"publisher","unstructured":"F\u00e1vero, L.P., Belfiore, P.: Binary and multinomial logistic regression models (2019). https:\/\/doi.org\/10.1016\/B978-0-12-811216-8.00014-8","DOI":"10.1016\/B978-0-12-811216-8.00014-8"},{"key":"7_CR6","doi-asserted-by":"publisher","first-page":"2628","DOI":"10.1093\/bioinformatics\/btz931","volume":"36","author":"SX Ge","year":"2020","unstructured":"Ge, S.X., Jung, D., Yao, R.: ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics 36, 2628\u20132629 (2020). https:\/\/doi.org\/10.1093\/bioinformatics\/btz931","journal-title":"Bioinformatics"},{"key":"7_CR7","doi-asserted-by":"publisher","first-page":"9669","DOI":"10.1038\/s41598-022-13665-5","volume":"12","author":"J Hong","year":"2022","unstructured":"Hong, J., Hachem, L.D., Fehlings, M.G.: A deep learning model to classify neoplastic state and tissue origin from transcriptomic data. Sci. Rep. 12, 9669 (2022). https:\/\/doi.org\/10.1038\/s41598-022-13665-5","journal-title":"Sci. Rep."},{"key":"7_CR8","unstructured":"Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings (2014). https:\/\/arxiv.org\/abs\/1412.6980v9"},{"key":"7_CR9","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1186\/1471-2105-12-323","volume":"12","author":"B Li","year":"2011","unstructured":"Li, B., Dewey, C.N.: RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 12, 323 (2011). https:\/\/doi.org\/10.1186\/1471-2105-12-323","journal-title":"BMC Bioinform."},{"key":"7_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/S12864-017-3906-0","volume":"18","author":"Y Li","year":"2017","unstructured":"Li, Y., et al.: A comprehensive genomic pan-cancer classification using The Cancer Genome Atlas gene expression data. BMC Genomics 18, 1\u201313 (2017). https:\/\/doi.org\/10.1186\/S12864-017-3906-0","journal-title":"BMC Genomics"},{"issue":"6","key":"7_CR11","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1038\/ng.2653","volume":"45","author":"J Lonsdale","year":"2013","unstructured":"Lonsdale, J., et al.: The genotype-tissue expression (GTEx) project. Nat. Genet. 45(6), 580\u2013585 (2013). https:\/\/doi.org\/10.1038\/ng.2653","journal-title":"Nat. Genet."},{"key":"7_CR12","doi-asserted-by":"publisher","unstructured":"Lyu, B., Haque, A.: Deep learning based tumor type classification using gene expression data. bioRxiv p. 364323 (2018). https:\/\/doi.org\/10.1101\/364323","DOI":"10.1101\/364323"},{"issue":"5","key":"7_CR13","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1186\/s12920-020-0677-2","volume":"13","author":"M Mostavi","year":"2020","unstructured":"Mostavi, M., Chiu, Y.C., Huang, Y., Chen, Y.: Convolutional neural network models for cancer type prediction based on gene expression. BMC Med. Genom. 13(5), 44 (2020). https:\/\/doi.org\/10.1186\/s12920-020-0677-2","journal-title":"BMC Med. Genom."},{"key":"7_CR14","doi-asserted-by":"publisher","first-page":"599","DOI":"10.3389\/fgene.2019.00599","volume":"10","author":"TP Quinn","year":"2019","unstructured":"Quinn, T.P., Nguyen, T., Lee, S.C., Venkatesh, S.: Cancer as a tissue anomaly: classifying tumor transcriptomes based only on healthy data. Front. Genet. 10, 599 (2019). https:\/\/doi.org\/10.3389\/fgene.2019.00599","journal-title":"Front. Genet."},{"key":"7_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fphy.2020.00203","volume":"8","author":"R Ramirez","year":"2020","unstructured":"Ramirez, R., et al.: Classification of cancer types using graph convolutional neural networks. Front. Phys. 8, 1\u201314 (2020). https:\/\/doi.org\/10.3389\/fphy.2020.00203","journal-title":"Front. Phys."},{"key":"7_CR16","doi-asserted-by":"publisher","first-page":"105524","DOI":"10.1016\/j.asoc.2019.105524","volume":"97","author":"D Singh","year":"2020","unstructured":"Singh, D., Singh, B.: Investigating the impact of data normalization on classification performance. Appl. Soft Comput. 97, 105524 (2020). https:\/\/doi.org\/10.1016\/j.asoc.2019.105524","journal-title":"Appl. Soft Comput."},{"key":"7_CR17","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1080\/21553769.2016.1178180","volume":"9","author":"R Tripathi","year":"2016","unstructured":"Tripathi, R., Sharma, P., Chakraborty, P., Varadwaj, P.K.: Next-generation sequencing revolution through big data analytics. Front. Life Sci. 9, 119\u2013149 (2016). https:\/\/doi.org\/10.1080\/21553769.2016.1178180","journal-title":"Front. Life Sci."},{"key":"7_CR18","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1038\/nbt.3772","volume":"35","author":"J Vivian","year":"2017","unstructured":"Vivian, J., et al.: Toil enables reproducible, open source, big biomedical data analyses. Nat. Biotechnol. 35, 314\u2013316 (2017). https:\/\/doi.org\/10.1038\/nbt.3772","journal-title":"Nat. Biotechnol."},{"key":"7_CR19","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.61","volume":"5","author":"Q Wang","year":"2018","unstructured":"Wang, Q., et al.: Unifying cancer and normal RNA sequencing data from different sources. Sci. Data 5, 180061 (2018). https:\/\/doi.org\/10.1038\/sdata.2018.61","journal-title":"Sci. Data"}],"container-title":["Lecture Notes in Computer Science","Computational Mathematics Modeling in Cancer Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-17266-3_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T10:55:52Z","timestamp":1710240952000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17266-3_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031172656","9783031172663"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17266-3_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"22 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CMMCA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Computational Mathematics Modeling in Cancer Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","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":"cmmca2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cmmca2022.casconf.cn\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","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":"15","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":"94% - 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)"}},{"value":"Due to the COVID-19 pandemic restrictions, the CMMCA2022 was held virtually.","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)"}}]}}