{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T05:52:00Z","timestamp":1743054720668,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031097256"},{"type":"electronic","value":"9783031097263"}],"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\/978-3-031-09726-3_20","type":"book-chapter","created":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T17:03:59Z","timestamp":1656176639000},"page":"219-229","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Multi-constraint Deep Semi-supervised Learning Method for Ovarian Cancer Prognosis Prediction"],"prefix":"10.1007","author":[{"given":"Hua","family":"Chai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longyi","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minfan","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongyue","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuedong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,26]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, 71(3), 209\u201349 (2017)","DOI":"10.3322\/caac.21660"},{"issue":"2","key":"20_CR2","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","volume":"34","author":"D Cox","year":"1972","unstructured":"Cox, D.: Regression models and life-tables. J. Roy. Stat. Soc. B 34(2), 187\u2013202 (1972)","journal-title":"J. Roy. Stat. Soc. B"},{"key":"20_CR3","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.artmed.2017.06.005","volume":"79","author":"H Wang","year":"2017","unstructured":"Wang, H., Zhou, L.: Random survival forest with space extensions for censored data. Artif. Intell. Med. 79, 52\u201361 (2017)","journal-title":"Artif. Intell. Med."},{"key":"20_CR4","doi-asserted-by":"publisher","first-page":"113334","DOI":"10.1016\/j.eswa.2020.113334","volume":"152","author":"Q Wang","year":"2020","unstructured":"Wang, Q., Zhou, Y., Zhang, W., Tang, Z., Chen, X.: Adaptive sampling using self-paced learning for imbalanced cancer data pre-diagnosis. Expert Syst. Appl. 152, 113334 (2020)","journal-title":"Expert Syst. Appl."},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Jhajharia, S., Varshney, H.K., Verma, S., Kumar, R. (eds.) A neural network based breast cancer prognosis model with PCA processed features. In: 2016 International Conference on Advances in Computing, Communications and Informatics (2016)","DOI":"10.1109\/ICACCI.2016.7732327"},{"issue":"5","key":"20_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v039.i05","volume":"39","author":"N Simon","year":"2011","unstructured":"Simon, N., Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for Cox\u2019s proportional hazards model via coordinate descent. J. Stat. Softw. 39(5), 1\u201313 (2011)","journal-title":"J. Stat. Softw."},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Wang, W., Liu, W.: PCLasso: a protein complex-based, group lasso-Cox model for accurate prognosis and risk protein complex discovery. Briefings in Bioinf. (2021)","DOI":"10.1093\/bib\/bbab212"},{"key":"20_CR8","doi-asserted-by":"publisher","first-page":"104481","DOI":"10.1016\/j.compbiomed.2021.104481","volume":"134","author":"H Chai","year":"2016","unstructured":"Chai, H., Zhou, X., Zhang, Z., Rao, J., Zhao, H., Yang, Y.: Integrating multi-omics data through deep learning for accurate cancer prognosis prediction. Comput. Biol. Med. 134, 104481 (2016)","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"20_CR9","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s12874-018-0482-1","volume":"18","author":"JL Katzman","year":"2018","unstructured":"Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., Kluger, Y.: DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol. 18(1), 24 (2018)","journal-title":"BMC Med Res Methodol."},{"issue":"6","key":"20_CR10","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.1158\/1078-0432.CCR-17-0853","volume":"24","author":"K Chaudhary","year":"2018","unstructured":"Chaudhary, K., Poirion, O.B., Lu, L., Garmire, L.X.: Deep learning\u2013based multi-omics integration robustly predicts survival in liver cancer. Clin. Cancer Res. 24(6), 1248\u20131259 (2018)","journal-title":"Clin. Cancer Res."},{"issue":"3","key":"20_CR11","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/s42514-021-00074-9","volume":"3","author":"H Chai","year":"2021","unstructured":"Chai, H., Zhang, Z., Wang, Y., Yang, Y.: Predicting bladder cancer prognosis by integrating multi-omics data through a transfer learning-based Cox proportional hazards network. CCF Trans. High Perform. Comput. 3(3), 311\u2013319 (2021). https:\/\/doi.org\/10.1007\/s42514-021-00074-9","journal-title":"CCF Trans. High Perform. Comput."},{"issue":"1","key":"20_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-20167-3","volume":"11","author":"YL Qiu","year":"2020","unstructured":"Qiu, Y.L., Zheng, H., Devos, A., Selby, H., Gevaert, O.: A meta-learning approach for genomic survival analysis. Nat. Commun. 11(1), 1\u201311 (2020)","journal-title":"Nat. Commun."},{"issue":"7","key":"20_CR13","doi-asserted-by":"publisher","first-page":"e47","DOI":"10.1093\/nar\/gkv007","volume":"43","author":"ME Ritchie","year":"2015","unstructured":"Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., et al.: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43(7), e47 (2015)","journal-title":"Nucleic Acids Res."},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, Q.: A survey on multi-task learning. IEEE Trans. Knowl. Data Eng. (2021)","DOI":"10.1109\/TKDE.2021.3070203"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Guo, X., Gao, L., Liu, X., Yin, J. (eds.) Improved Deep Embedded Clustering with Local Structure Preservation. Ijcai (2017)","DOI":"10.24963\/ijcai.2017\/243"}],"container-title":["Lecture Notes in Computer Science","Advances in Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-09726-3_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T20:25:17Z","timestamp":1727468717000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-09726-3_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031097256","9783031097263"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-09726-3_20","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":"26 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Sensing and Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"15 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icsi2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iasei.org\/icsi2022\/","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"171","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":"85","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":"50% - 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":"2.6","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":"3.5","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)"}}]}}