{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:06:25Z","timestamp":1742918785671,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031789762"},{"type":"electronic","value":"9783031789779"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-78977-9_16","type":"book-chapter","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T10:12:39Z","timestamp":1737972759000},"page":"244-259","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Latent Embedding Based on a Transcription-Decay Decomposition of mRNA Dynamics Using Self-supervised CoxPH"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0796-8985","authenticated-orcid":false,"given":"Martin","family":"\u0160pendl","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4888-7256","authenticated-orcid":false,"given":"Toma\u017e","family":"Curk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5864-7056","authenticated-orcid":false,"given":"Bla\u017e","family":"Zupan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,28]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019)","DOI":"10.1145\/3292500.3330701"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Chen, T., He, H.L., Church, G.M.: Modeling gene expression with differential equations. In: Biocomputing\u201999, pp. 29\u201340. World Scientific (1999)","DOI":"10.1142\/9789814447300_0004"},{"issue":"21","key":"16_CR3","doi-asserted-by":"publisher","first-page":"2474","DOI":"10.1093\/bioinformatics\/btn458","volume":"24","author":"X Chen","year":"2008","unstructured":"Chen, X., Wang, L., Smith, J.D., Zhang, B.: Supervised principal component analysis for gene set enrichment of microarray data with continuous or survival outcomes. Bioinformatics 24(21), 2474\u20132481 (2008)","journal-title":"Bioinformatics"},{"issue":"7403","key":"16_CR4","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1038\/nature10983","volume":"486","author":"C Curtis","year":"2012","unstructured":"Curtis, C., et al.: The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486(7403), 346\u2013352 (2012)","journal-title":"Nature"},{"key":"16_CR5","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Doncevic, D., Herrmann, C.: Biologically informed variational autoencoders allow predictive modeling of genetic and drug-induced perturbations. Bioinformatics 39(6), btad387 (2023)","DOI":"10.1093\/bioinformatics\/btad387"},{"issue":"7757","key":"16_CR7","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1038\/s41586-019-1186-3","volume":"569","author":"M Ghandi","year":"2019","unstructured":"Ghandi, M., et al.: Next-generation characterization of the cancer cell line encyclopedia. Nature 569(7757), 503\u2013508 (2019)","journal-title":"Nature"},{"issue":"16","key":"16_CR8","doi-asserted-by":"publisher","first-page":"4415","DOI":"10.1093\/bioinformatics\/btaa293","volume":"36","author":"CH Gr\u00f8nbech","year":"2020","unstructured":"Gr\u00f8nbech, C.H., Vording, M.F., Timshel, P.N., S\u00f8nderby, C.K., Pers, T.H., Winther, O.: scVAE: variational auto-encoders for single-cell gene expression data. Bioinformatics 36(16), 4415\u20134422 (2020)","journal-title":"Bioinformatics"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Im\u00a0Im, D., Ahn, S., Memisevic, R., Bengio, Y.: Denoising criterion for variational auto-encoding framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a031 (2017)","DOI":"10.1609\/aaai.v31i1.10777"},{"issue":"6","key":"16_CR10","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1038\/s42254-021-00314-5","volume":"3","author":"GE Karniadakis","year":"2021","unstructured":"Karniadakis, G.E., Kevrekidis, I.G., Lu, L., Perdikaris, P., Wang, S., Yang, L.: Physics-informed machine learning. Nature Rev. Phys. 3(6), 422\u2013440 (2021)","journal-title":"Nature Rev. Phys."},{"key":"16_CR11","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"issue":"12","key":"16_CR12","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1038\/s41592-018-0229-2","volume":"15","author":"R Lopez","year":"2018","unstructured":"Lopez, R., Regier, J., Cole, M.B., Jordan, M.I., Yosef, N.: Deep generative modeling for single-cell transcriptomics. Nat. Methods 15(12), 1053\u20131058 (2018)","journal-title":"Nat. Methods"},{"issue":"4","key":"16_CR13","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1038\/nmeth.4627","volume":"15","author":"J Ma","year":"2018","unstructured":"Ma, J., et al.: Using deep learning to model the hierarchical structure and function of a cell. Nat. Methods 15(4), 290\u2013298 (2018)","journal-title":"Nat. Methods"},{"key":"16_CR14","unstructured":"Van\u00a0der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)"},{"issue":"7","key":"16_CR15","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1016\/j.cell.2018.05.056","volume":"173","author":"KY Michael","year":"2018","unstructured":"Michael, K.Y., Ma, J., Fisher, J., Kreisberg, J.F., Raphael, B.J., Ideker, T.: Visible machine learning for biomedicine. Cell 173(7), 1562\u20131565 (2018)","journal-title":"Cell"},{"issue":"1\/2","key":"16_CR16","doi-asserted-by":"publisher","first-page":"17","DOI":"10.2307\/2332142","volume":"37","author":"PA Moran","year":"1950","unstructured":"Moran, P.A.: Notes on continuous stochastic phenomena. Biometrika 37(1\/2), 17\u201323 (1950)","journal-title":"Biometrika"},{"key":"16_CR17","unstructured":"Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. In: International Conference on Machine Learning, pp. 1278\u20131286. PMLR (2014)"},{"issue":"1","key":"16_CR18","doi-asserted-by":"publisher","first-page":"5684","DOI":"10.1038\/s41467-021-26017-0","volume":"12","author":"L Seninge","year":"2021","unstructured":"Seninge, L., Anastopoulos, I., Ding, H., Stuart, J.: VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics. Nat. Commun. 12(1), 5684 (2021)","journal-title":"Nat. Commun."},{"issue":"6","key":"16_CR19","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1016\/j.cell.2017.10.049","volume":"171","author":"A Subramanian","year":"2017","unstructured":"Subramanian, A., et al.: A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171(6), 1437\u20131452 (2017)","journal-title":"Cell"},{"key":"16_CR20","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), 209\u2013249 (2021)","DOI":"10.3322\/caac.21660"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Swanson, K., Wu, E., Zhang, A., Alizadeh, A.A., Zou, J.: From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. Cell (2023)","DOI":"10.1016\/j.cell.2023.01.035"},{"issue":"12","key":"16_CR22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0219102","volume":"14","author":"L Vidman","year":"2019","unstructured":"Vidman, L., K\u00e4llberg, D., Ryd\u00e9n, P.: Cluster analysis on high dimensional RNA-seq data with applications to cancer research-an evaluation study. PLoS ONE 14(12), e0219102 (2019)","journal-title":"PLoS ONE"},{"issue":"1","key":"16_CR23","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1186\/s12859-023-05262-8","volume":"24","author":"M Wysocka","year":"2023","unstructured":"Wysocka, M., Wysocki, O., Zufferey, M., Landers, D., Freitas, A.: A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data. BMC Bioinform. 24(1), 198 (2023)","journal-title":"BMC Bioinform."},{"key":"16_CR24","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s12864-017-4226-0","volume":"18","author":"R Xie","year":"2017","unstructured":"Xie, R., Wen, J., Quitadamo, A., Cheng, J., Shi, X.: A deep auto-encoder model for gene expression prediction. BMC Genomics 18, 39\u201349 (2017)","journal-title":"BMC Genomics"},{"issue":"D1","key":"16_CR25","doi-asserted-by":"publisher","first-page":"D955","DOI":"10.1093\/nar\/gks1111","volume":"41","author":"W Yang","year":"2012","unstructured":"Yang, W., et al.: Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41(D1), D955\u2013D961 (2012)","journal-title":"Nucleic Acids Res."},{"issue":"9","key":"16_CR26","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1093\/bioinformatics\/17.9.763","volume":"17","author":"KY Yeung","year":"2001","unstructured":"Yeung, K.Y., Ruzzo, W.L.: Principal component analysis for clustering gene expression data. Bioinformatics 17(9), 763\u2013774 (2001)","journal-title":"Bioinformatics"},{"issue":"2","key":"16_CR27","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1198\/106186006X113430","volume":"15","author":"H Zou","year":"2006","unstructured":"Zou, H., Hastie, T., Tibshirani, R.: Sparse principal component analysis. J. Comput. Graph. Stat. 15(2), 265\u2013286 (2006)","journal-title":"J. Comput. Graph. Stat."}],"container-title":["Lecture Notes in Computer Science","Discovery Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78977-9_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T10:12:46Z","timestamp":1737972766000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78977-9_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031789762","9783031789779"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78977-9_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"28 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Discovery Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pisa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dis2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ds2024.isti.cnr.it\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}