{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T07:13:44Z","timestamp":1775027624427,"version":"3.50.1"},"reference-count":89,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"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":["Nat Mach Intell"],"DOI":"10.1038\/s42256-020-00232-8","type":"journal-article","created":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T16:07:04Z","timestamp":1602518824000},"page":"619-628","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions"],"prefix":"10.1038","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0083-6994","authenticated-orcid":false,"given":"Anthony","family":"Culos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2380-6424","authenticated-orcid":false,"given":"Amy S.","family":"Tsai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natalie","family":"Stanley","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Becker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad S.","family":"Ghaemi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David R.","family":"McIlwain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramin","family":"Fallahzadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Athena","family":"Tanada","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huda","family":"Nassar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Camilo","family":"Espinosa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Xenochristou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edward","family":"Ganio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura","family":"Peterson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6394-3055","authenticated-orcid":false,"given":"Xiaoyuan","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9974-4661","authenticated-orcid":false,"given":"Ina A.","family":"Stelzer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuo","family":"Ando","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3017-5939","authenticated-orcid":false,"given":"Dyani","family":"Gaudilliere","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9245-6686","authenticated-orcid":false,"given":"Thanaphong","family":"Phongpreecha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9441-521X","authenticated-orcid":false,"given":"Ivana","family":"Mari\u0107","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alan L.","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gary M.","family":"Shaw","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David K.","family":"Stevenson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1341-2453","authenticated-orcid":false,"given":"Sean","family":"Bendall","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kara L.","family":"Davis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wendy","family":"Fantl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8862-9043","authenticated-orcid":false,"given":"Garry P.","family":"Nolan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trevor","family":"Hastie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Tibshirani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1550-8136","authenticated-orcid":false,"given":"Martin S.","family":"Angst","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3475-5706","authenticated-orcid":false,"given":"Brice","family":"Gaudilliere","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6117-8764","authenticated-orcid":false,"given":"Nima","family":"Aghaeepour","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"232_CR1","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1038\/ni.3768","volume":"18","author":"MM Davis","year":"2017","unstructured":"Davis, M. M., Tato, C. M. & Furman, D. Systems immunology: just getting started. Nat. Immunol. 18, 725\u2013732 (2017).","journal-title":"Nat. Immunol."},{"key":"232_CR2","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/ni.3693","volume":"18","author":"JC Rieckmann","year":"2017","unstructured":"Rieckmann, J. C. et al. Social network architecture of human immune cells unveiled by quantitative proteomics. Nat. Immunol. 18, 583\u2013593 (2017).","journal-title":"Nat. Immunol."},{"key":"232_CR3","doi-asserted-by":"publisher","unstructured":"Mathew, D. et al. Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications. Science (2020); https:\/\/doi.org\/10.1126\/science.abc8511.","DOI":"10.1126\/science.abc8511"},{"key":"232_CR4","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1038\/s41591-020-0944-y","volume":"26","author":"AJ Wilk","year":"2020","unstructured":"Wilk, A. J. et al. A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat. Med. 26, 1070\u20131076 (2020).","journal-title":"Nat. Med."},{"key":"232_CR5","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1056\/NEJMoa1103849","volume":"365","author":"DL Porter","year":"2011","unstructured":"Porter, D. L., Levine, B. L., Kalos, M., Bagg, A. & June, C. H. Chimeric antigen receptor-modified T cells in chronic lymphoid leukemia. New Engl. J. Med. 365, 725\u2013733 (2011).","journal-title":"New Engl. J. Med."},{"key":"232_CR6","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1038\/s41590-018-0232-x","volume":"19","author":"JK Ryu","year":"2018","unstructured":"Ryu, J. K. et al. Fibrin-targeting immunotherapy protects against neuroinflammation and neurodegeneration. Nat. Immunol. 19, 1212\u20131223 (2018).","journal-title":"Nat. Immunol."},{"key":"232_CR7","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1038\/s41590-018-0233-9","volume":"19","author":"EO Saphire","year":"2018","unstructured":"Saphire, E. O., Schendel, S. L., Gunn, B. M., Milligan, J. C. & Alter, G. Antibody-mediated protection against Ebola virus. Nat. Immunol. 19, 1169\u20131178 (2018).","journal-title":"Nat. Immunol."},{"key":"232_CR8","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1002\/cyto.a.10072","volume":"55","author":"PO Krutzik","year":"2003","unstructured":"Krutzik, P. O. & Nolan, G. P. Intracellular phospho-protein staining techniques for flow cytometry: monitoring single cell signaling events. Cytometry A 55, 61\u201370 (2003).","journal-title":"Cytometry A"},{"key":"232_CR9","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1002\/cyto.a.23608","volume":"93","author":"L Nettey","year":"2018","unstructured":"Nettey, L., Giles, A. J. & Chattopadhyay, P. K. OMIP-050: a 28-color\/30-parameter fluorescence flow cytometry panel to enumerate and characterize cells expressing a wide array of immune checkpoint molecules. Cytometry A 93, 1094\u20131096 (2018).","journal-title":"Cytometry A"},{"key":"232_CR10","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1146\/annurev-anchem-061417-125927","volume":"12","author":"PK Chattopadhyay","year":"2019","unstructured":"Chattopadhyay, P. K., Winters, A. F., Lomas, W. E., Laino, A. S. & Woods, D. M. High-parameter single-cell analysis. Annu. Rev. Anal. Chem. 12, 411\u2013430 (2019).","journal-title":"Annu. Rev. Anal. Chem."},{"key":"232_CR11","doi-asserted-by":"crossref","first-page":"6813","DOI":"10.1021\/ac901049w","volume":"81","author":"DR Bandura","year":"2009","unstructured":"Bandura, D. R. et al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal. Chem. 81, 6813\u20136822 (2009).","journal-title":"Anal. Chem."},{"key":"232_CR12","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1126\/science.1198704","volume":"332","author":"SC Bendall","year":"2011","unstructured":"Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687\u2013696 (2011).","journal-title":"Science"},{"key":"232_CR13","doi-asserted-by":"crossref","DOI":"10.1038\/srep20686","volume":"6","author":"G Finak","year":"2016","unstructured":"Finak, G. et al. Standardizing flow cytometry immunophenotyping analysis from the human immunophenotyping consortium. Sci. Rep. 6, 20686 (2016).","journal-title":"Sci. Rep."},{"key":"232_CR14","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1038\/ni.3485","volume":"17","author":"EW Newell","year":"2016","unstructured":"Newell, E. W. & Cheng, Y. Mass cytometry: blessed with the curse of dimensionality. Nat. Immunol. 17, 890\u2013895 (2016).","journal-title":"Nat. Immunol."},{"key":"232_CR15","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/34.824819","volume":"22","author":"AK Jain","year":"2000","unstructured":"Jain, A. K., Duin, P. W. & Mao, Jianchang Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22, 4\u201337 (2000).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"232_CR16","unstructured":"Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference and Prediction 2nd edn (Springer, 2016)."},{"key":"232_CR17","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206\u2013215 (2019).","journal-title":"Nat. Mach. Intell."},{"key":"232_CR18","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1038\/s42256-019-0140-2","volume":"2","author":"J Li","year":"2020","unstructured":"Li, J., Liu, L., Le, T. D. & Liu, J. Accurate data-driven prediction does not mean high reproducibility. Nat. Mach. Intell. 2, 13\u201315 (2020).","journal-title":"Nat. Mach. Intell."},{"key":"232_CR19","unstructured":"Krupka, E. & Tishby, N. Incorporating prior knowledge on features into learning. In Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (eds Meila, M. & Shen, X.) Vol. 2, 227\u2013234 (PMLR, 2007)."},{"key":"232_CR20","unstructured":"Mollaysa, A., Strasser, P. & Kalousis, A. Regularising non-linear models using feature side-information. In Proceedings of the 34th International Conference on Machine Learning (eds Precup, D. & Teh, Y. W.) Vol. 70, 2508\u20132517 (PMLR, 2017)."},{"key":"232_CR21","doi-asserted-by":"crossref","first-page":"3170","DOI":"10.1093\/bioinformatics\/btm488","volume":"23","author":"F Tai","year":"2007","unstructured":"Tai, F. & Pan, W. Incorporating prior knowledge of gene functional groups into regularized discriminant analysis of microarray data. Bioinformatics 23, 3170\u20133177 (2007).","journal-title":"Bioinformatics"},{"key":"232_CR22","doi-asserted-by":"publisher","unstructured":"Bergersen, L. C., Glad, I. K. & Lyng, H. Weighted LASSO with data integration. Stat. Appl. Genet. Mol. Biol. 10 (2011); https:\/\/doi.org\/10.2202\/1544-6115.1703","DOI":"10.2202\/1544-6115.1703"},{"key":"232_CR23","doi-asserted-by":"crossref","first-page":"i154","DOI":"10.1093\/bioinformatics\/btz338","volume":"35","author":"L Handl","year":"2019","unstructured":"Handl, L., Jalali, A., Scherer, M., Eggeling, R. & Pfeifer, N. Weighted elastic net for unsupervised domain adaptation with application to age prediction from DNA methylation data. Bioinformatics 35, i154\u2013i163 (2019).","journal-title":"Bioinformatics"},{"key":"232_CR24","doi-asserted-by":"publisher","unstructured":"Zuo, Y., Yu, G. & Ressom, H. W. Integrating prior biological knowledge and graphical LASSO for network inference. In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 1543\u20131547 (IEEE, 2015); https:\/\/doi.org\/10.1109\/BIBM.2015.7359905","DOI":"10.1109\/BIBM.2015.7359905"},{"key":"232_CR25","doi-asserted-by":"crossref","unstructured":"Guan, X. & Liu, L. Know-GRRF: domain-knowledge informed biomarker discovery with random forests. In Bioinformatics and Biomedical Engineering: 6th International Work-Conference, IWBBIO 2018, Granada, Spain, 2018, Proceedings, Part II (eds Rojas, I. & Ortu\u00f1o, F.) Vol. 10814, 3\u201314 (2018).","DOI":"10.1007\/978-3-319-78759-6_1"},{"key":"232_CR26","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1631\/jzus.C1200205","volume":"14","author":"J Shi","year":"2013","unstructured":"Shi, J., Zhang, S. & Qiu, L. Credit scoring by feature-weighted support vector machines. J. Zhejiang Univ. Sci. C 14, 197\u2013204 (2013).","journal-title":"J. Zhejiang Univ. Sci. C"},{"key":"232_CR27","doi-asserted-by":"publisher","unstructured":"Sarafianos, N., Vrigkas, M. & Kakadiaris, I. A. Adaptive SVM+: learning with privileged information for domain adaptation. In Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) 2637\u20132644 (IEEE, 2017); https:\/\/doi.org\/10.1109\/ICCVW.2017.313","DOI":"10.1109\/ICCVW.2017.313"},{"key":"232_CR28","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s12543-009-0022-0","volume":"1","author":"H Xing","year":"2009","unstructured":"Xing, H., Ha, M., Hu, B. & Tian, D. Linear feature-weighted support vector machine. Fuzzy Inf. Eng. 1, 289\u2013305 (2009).","journal-title":"Fuzzy Inf. Eng."},{"key":"232_CR29","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.patcog.2017.01.018","volume":"66","author":"G Bhattacharya","year":"2017","unstructured":"Bhattacharya, G., Ghosh, K. & Chowdhury, A. S. Granger causality driven AHP for feature weighted knn. Pattern Recogn. 66, 425\u2013436 (2017).","journal-title":"Pattern Recogn."},{"key":"232_CR30","unstructured":"Mollaysa, A., Kalousis, A., Bruno, E. & Diephuis, M. Learning to augment with feature side-information. In Proceedings of the 11th Asian Conference on Machine Learning (PMLR) Vol. 101, 173\u2013187 (PMLR, 2019)."},{"key":"232_CR31","unstructured":"Ye, Y., Li, H., Deng, X. & Huang, J. Z. Feature Weighting Random Forest for Detection of Hidden Web Search Interfaces (ACL, 2008); https:\/\/www.aclweb.org\/anthology\/O08-6001.pdf"},{"key":"232_CR32","volume":"1","author":"W Zhang","year":"2017","unstructured":"Zhang, W., Chien, J., Yong, J. & Kuang, R. Network-based machine learning and graph theory algorithms for precision oncology. NPJ Precis. Oncol. 1, 25 (2017).","journal-title":"NPJ Precis. Oncol."},{"key":"232_CR33","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1039\/C4IB00124A","volume":"6","author":"S Sinha","year":"2014","unstructured":"Sinha, S. Integration of prior biological knowledge and epigenetic information enhances the prediction accuracy of the Bayesian Wnt pathway. Integr. Biol. (Camb.) 6, 1034\u20131048 (2014).","journal-title":"Integr. Biol."},{"key":"232_CR34","doi-asserted-by":"crossref","first-page":"2988","DOI":"10.1093\/bioinformatics\/btw363","volume":"32","author":"F Fabris","year":"2016","unstructured":"Fabris, F. & Freitas, A. A. New KEGG pathway-based interpretable features for classifying ageing-related mouse proteins. Bioinformatics 32, 2988\u20132995 (2016).","journal-title":"Bioinformatics"},{"key":"232_CR35","doi-asserted-by":"publisher","unstructured":"Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B (2005); https:\/\/doi.org\/10.1111\/j.1467-9868.2005.00527.x","DOI":"10.1111\/j.1467-9868.2005.00527.x"},{"key":"232_CR36","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1177\/0022343317691330","volume":"54","author":"H Hegre","year":"2017","unstructured":"Hegre, H., Metternich, N. W., Nyg\u00e5rd, H. M. & Wucherpfennig, J. Introduction. J. Peace Res. 54, 113\u2013124 (2017).","journal-title":"J. Peace Res."},{"key":"232_CR37","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-019-12928-6","volume":"10","author":"NS Madhukar","year":"2019","unstructured":"Madhukar, N. S. et al. A Bayesian machine learning approach for drug target identification using diverse data types. Nat. Commun. 10, 5221 (2019).","journal-title":"Nat. Commun."},{"key":"232_CR38","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1038\/nrd2110","volume":"5","author":"NE Sharpless","year":"2006","unstructured":"Sharpless, N. E. & Depinho, R. A. The mighty mouse: genetically engineered mouse models in cancer drug development. Nat. Rev. Drug Discov. 5, 741\u2013754 (2006).","journal-title":"Nat. Rev. Drug Discov."},{"key":"232_CR39","volume":"10","author":"F Zhu","year":"2019","unstructured":"Zhu, F., Nair, R. R., Fisher, E. M. C. & Cunningham, T. J. Humanising the mouse genome piece by piece. Nat. Commun. 10, 1845 (2019).","journal-title":"Nat. Commun."},{"key":"232_CR40","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1111\/j.1467-9868.2007.00627.x","volume":"70","author":"L Meier","year":"2008","unstructured":"Meier, L., Van De Geer, S. & B\u00fchlmann, P. The group lasso for logistic regression. J. R. Stat. Soc. B 70, 53\u201371 (2008).","journal-title":"J. R. Stat. Soc. B"},{"key":"232_CR41","doi-asserted-by":"publisher","unstructured":"Velten, B. & Huber, W. Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes. Biostatistics (2019); https:\/\/doi.org\/10.1093\/biostatistics\/kxz034.","DOI":"10.1093\/biostatistics\/kxz034"},{"key":"232_CR42","doi-asserted-by":"publisher","unstructured":"Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002); https:\/\/doi.org\/10.1007\/978-0-387-21706-2","DOI":"10.1007\/978-0-387-21706-2"},{"key":"232_CR43","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273\u2013297 (1995).","journal-title":"Mach. Learn."},{"key":"232_CR44","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. Learning 45, 5\u201332 (2001).","journal-title":"Mach. Learning"},{"key":"232_CR45","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. B 58, 267\u2013288 (1996).","journal-title":"J. R. Stat. Soc. B"},{"key":"232_CR46","doi-asserted-by":"crossref","first-page":"25","DOI":"10.2202\/1544-6115.1309","volume":"6","author":"MJ van der Laan","year":"2007","unstructured":"van der Laan, M. J., Polley, E. C. & Hubbard, A. E. Super learner. Stat. Appl. Genet. Mol. Biol. 6, 25 (2007).","journal-title":"Stat. Appl. Genet. Mol. Biol."},{"key":"232_CR47","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/366583a0","volume":"366","author":"O Silvennoinen","year":"1993","unstructured":"Silvennoinen, O., Ihle, J. N., Schlessinger, J. & Levy, D. E. Interferon-induced nuclear signalling by Jak protein tyrosine kinases. Nature 366, 583\u2013585 (1993).","journal-title":"Nature"},{"key":"232_CR48","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1038\/nri3581","volume":"14","author":"LB Ivashkiv","year":"2014","unstructured":"Ivashkiv, L. B. & Donlin, L. T. Regulation of type I interferon responses. Nat. Rev. Immunol. 14, 36\u201349 (2014).","journal-title":"Nat. Rev. Immunol."},{"key":"232_CR49","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1038\/nri3156","volume":"12","author":"O Boyman","year":"2012","unstructured":"Boyman, O. & Sprent, J. The role of interleukin-2 during homeostasis and activation of the immune system. Nat. Rev. Immunol. 12, 180\u2013190 (2012).","journal-title":"Nat. Rev. Immunol."},{"key":"232_CR50","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1038\/ni.3153","volume":"16","author":"CA Hunter","year":"2015","unstructured":"Hunter, C. A. & Jones, S. A. IL-6 as a keystone cytokine in health and disease. Nat. Immunol. 16, 448\u2013457 (2015).","journal-title":"Nat. Immunol."},{"key":"232_CR51","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1182\/blood-2008-07-019307","volume":"113","author":"BA Beutler","year":"2009","unstructured":"Beutler, B. A. TLRs and innate immunity. Blood 113, 1399\u20131407 (2009).","journal-title":"Blood"},{"key":"232_CR52","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.immuni.2005.08.010","volume":"23","author":"JM Park","year":"2005","unstructured":"Park, J. M. et al. Signaling pathways and genes that inhibit pathogen-induced macrophage apoptosis\u2013CREB and NF-kB as key regulators. Immunity 23, 319\u2013329 (2005).","journal-title":"Immunity"},{"key":"232_CR53","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1084\/jem.194.6.863","volume":"194","author":"N Kadowaki","year":"2001","unstructured":"Kadowaki, N. et al. Subsets of human dendritic cell precursors express different toll-like receptors and respond to different microbial antigens. J. Exp. Med. 194, 863\u2013869 (2001).","journal-title":"J. Exp. Med."},{"key":"232_CR54","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1038\/icb.2013.99","volume":"92","author":"M Adib-Conquy","year":"2014","unstructured":"Adib-Conquy, M., Scott-Algara, D., Cavaillon, J.-M. & Souza-Fonseca-Guimaraes, F. TLR-mediated activation of NK cells and their role in bacterial\/viral immune responses in mammals. Immunol. Cell Biol. 92, 256\u2013262 (2014).","journal-title":"Immunol. Cell Biol."},{"key":"232_CR55","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1084\/jem.20021633","volume":"197","author":"I Caramalho","year":"2003","unstructured":"Caramalho, I. et al. Regulatory T cells selectively express Toll-like receptors and are activated by lipopolysaccharide. J. Exp. Med. 197, 403\u2013411 (2003).","journal-title":"J. Exp. Med."},{"key":"232_CR56","doi-asserted-by":"crossref","first-page":"eaan2946","DOI":"10.1126\/sciimmunol.aan2946","volume":"2","author":"N Aghaeepour","year":"2017","unstructured":"Aghaeepour, N. et al. An immune clock of human pregnancy.Sci. Immunol. 2, eaan2946 (2017).","journal-title":"Sci. Immunol."},{"key":"232_CR57","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1146\/annurev-pathmechdis-012418-012743","volume":"14","author":"H Deshmukh","year":"2018","unstructured":"Deshmukh, H. & Way, S. S. Immunological basis for recurrent fetal loss and pregnancy complications. Annu. Rev. Pathol. 14, 185\u2013210 (2018).","journal-title":"Annu. Rev. Pathol."},{"key":"232_CR58","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1038\/nm.3160","volume":"19","author":"PC Arck","year":"2013","unstructured":"Arck, P. C. & Hecher, K. Fetomaternal immune cross-talk and its consequences for maternal and offspring\u2019s health. Nat. Med. 19, 548\u2013556 (2013).","journal-title":"Nat. Med."},{"key":"232_CR59","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1126\/science.1251816","volume":"345","author":"R Romero","year":"2014","unstructured":"Romero, R., Dey, S. K. & Fisher, S. J. Preterm labor: one syndrome, many causes. Science 345, 760\u2013765 (2014).","journal-title":"Science"},{"key":"232_CR60","doi-asserted-by":"crossref","first-page":"eaay1059","DOI":"10.1126\/scitranslmed.aay1059","volume":"12","author":"AG Paquette","year":"2020","unstructured":"Paquette, A. G., Hood, L., Price, N. D. & Sadovsky, Y. Deep phenotyping during pregnancy for predictive and preventive medicine. Sci. Transl. Med. 12, eaay1059 (2020).","journal-title":"Sci. Transl. Med."},{"key":"232_CR61","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008).","journal-title":"J. Mach. Learn. Res."},{"key":"232_CR62","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1016\/S0140-6736(05)67728-8","volume":"366","author":"BL Pihlstrom","year":"2005","unstructured":"Pihlstrom, B. L., Michalowicz, B. S. & Johnson, N. W. Periodontal diseases. Lancet 366, 1809\u20131820 (2005).","journal-title":"Lancet"},{"key":"232_CR63","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1902\/jop.2015.140520","volume":"86","author":"PI Eke","year":"2015","unstructured":"Eke, P. I. et al. Update on prevalence of periodontitis in adults in the United States: NHANES 2009 to 2012. J. Periodontol. 86, 611\u2013622 (2015).","journal-title":"J. Periodontol."},{"key":"232_CR64","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1177\/0022034514552491","volume":"93","author":"NJ Kassebaum","year":"2014","unstructured":"Kassebaum, N. J. et al. Global burden of severe periodontitis in 1990\u20132010: a systematic review and meta-regression. J. Dent. Res. 93, 1045\u20131053 (2014).","journal-title":"J. Dent. Res."},{"key":"232_CR65","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","volume":"143","author":"JA Hanley","year":"1982","unstructured":"Hanley, J. A. & McNeil, B. J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29\u201336 (1982).","journal-title":"Radiology"},{"key":"232_CR66","unstructured":"Meyer, D., Dimitriadou, E., Hornik, K. & Leisch, F. Package e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071) (TU Wien, 2019)."},{"key":"232_CR67","doi-asserted-by":"publisher","unstructured":"Littmann, M. et al. Validity of machine learning in biology and medicine increased through collaborations across fields of expertise. Nat. Mach. Intell. (2020); https:\/\/doi.org\/10.1038\/s42256-019-0139-8.","DOI":"10.1038\/s42256-019-0139-8"},{"key":"232_CR68","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1016\/j.neunet.2009.06.042","volume":"22","author":"V Vapnik","year":"2009","unstructured":"Vapnik, V. & Vashist, A. A new learning paradigm: learning using privileged information. Neural Netw. 22, 544\u2013557 (2009).","journal-title":"Neural Netw."},{"key":"232_CR69","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1038\/nbt.4152","volume":"36","author":"K Kveler","year":"2018","unstructured":"Kveler, K. et al. Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed. Nat. Biotechnol. 36, 651\u2013659 (2018).","journal-title":"Nat. Biotechnol."},{"key":"232_CR70","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1038\/nmeth.2365","volume":"10","author":"N Aghaeepour","year":"2013","unstructured":"Aghaeepour, N. et al. Critical assessment of automated flow cytometry data analysis techniques. Nat. Methods 10, 228\u2013238 (2013).","journal-title":"Nat. Methods"},{"key":"232_CR71","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.1093\/bioinformatics\/bty082","volume":"34","author":"M Lux","year":"2018","unstructured":"Lux, M. et al. flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry. Bioinformatics 34, 2245\u20132253 (2018).","journal-title":"Bioinformatics"},{"key":"232_CR72","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.cell.2015.05.047","volume":"162","author":"JH Levine","year":"2015","unstructured":"Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184\u2013197 (2015).","journal-title":"Cell"},{"key":"232_CR73","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1002\/cyto.a.22625","volume":"87","author":"S Van Gassen","year":"2015","unstructured":"Van Gassen, S. et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87, 636\u2013645 (2015).","journal-title":"Cytometry A"},{"key":"232_CR74","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1038\/nbt.1991","volume":"29","author":"P Qiu","year":"2011","unstructured":"Qiu, P. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29, 886\u2013891 (2011).","journal-title":"Nat. Biotechnol."},{"key":"232_CR75","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1038\/nmeth.3863","volume":"13","author":"N Samusik","year":"2016","unstructured":"Samusik, N., Good, Z., Spitzer, M. H., Davis, K. L. & Nolan, G. P. Automated mapping of phenotype space with single-cell data. Nat. Methods 13, 493\u2013496 (2016).","journal-title":"Nat. Methods"},{"key":"232_CR76","volume":"11","author":"N Stanley","year":"2020","unstructured":"Stanley, N. et al. VoPo leverages cellular heterogeneity for predictive modeling of single-cell data. Nat. Commun. 11, 3738 (2020).","journal-title":"Nat. Commun."},{"key":"232_CR77","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1080\/21642583.2018.1482477","volume":"6","author":"X Ding","year":"2018","unstructured":"Ding, X. et al. Prior knowledge-based deep learning method for indoor object recognition and application. Syst. Sci. Control Eng. 6, 249\u2013257 (2018).","journal-title":"Syst. Sci. Control Eng."},{"key":"232_CR78","doi-asserted-by":"publisher","unstructured":"Xu, Z., Liu, B., Wang, B., Sun, C. & Wang, X. Incorporating loose-structured knowledge into conversation modeling via recall-gate LSTM. In Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN) 3506\u20133513 (IEEE, 2017); https:\/\/doi.org\/10.1109\/IJCNN.2017.7966297","DOI":"10.1109\/IJCNN.2017.7966297"},{"key":"232_CR79","doi-asserted-by":"publisher","unstructured":"Diligenti, M., Roychowdhury, S. & Gori, M. Integrating prior knowledge into deep learning. In Proceedings of the 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) 920\u2013923 (IEEE, 2017); https:\/\/doi.org\/10.1109\/ICMLA.2017.00-37","DOI":"10.1109\/ICMLA.2017.00-37"},{"key":"232_CR80","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1093\/bioinformatics\/bty537","volume":"35","author":"MS Ghaemi","year":"2019","unstructured":"Ghaemi, M. S. et al. Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics 35, 95\u2013103 (2019).","journal-title":"Bioinformatics"},{"key":"232_CR81","first-page":"55\u201367","volume":"12","author":"AE Hoerl","year":"1970","unstructured":"Hoerl, A. E. & Kennard, R. W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12, 55\u201367 (1970).","journal-title":"Technometrics"},{"key":"232_CR82","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1198\/jasa.2011.tm09241","volume":"106","author":"C Hans","year":"2011","unstructured":"Hans, C. Elastic net regression modeling with the orthant normal prior. J. Am. Stat. Assoc. 106, 1383\u20131393 (2011).","journal-title":"J. Am. Stat. Assoc."},{"key":"232_CR83","unstructured":"LeBeau, B. simglm: Simulate Models Based on the Generalized Linear Model (CRAN, 2019)."},{"key":"232_CR84","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1038\/nprot.2015.020","volume":"10","author":"ER Zunder","year":"2015","unstructured":"Zunder, E. R. et al. Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat. Protoc. 10, 316\u2013333 (2015).","journal-title":"Nat. Protoc."},{"key":"232_CR85","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1002\/cyto.a.22271","volume":"83","author":"R Finck","year":"2013","unstructured":"Finck, R. et al. Normalization of mass cytometry data with bead standards. Cytometry A 83, 483\u2013494 (2013).","journal-title":"Cytometry A"},{"key":"232_CR86","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1189\/jlb.5A0415-140R","volume":"100","author":"I Pacella","year":"2016","unstructured":"Pacella, I. et al. IFN-\u03b1 promotes rapid human Treg contraction and late Th1-like Treg decrease. J. Leukoc. Biol. 100, 613\u2013623 (2016).","journal-title":"J. Leukoc. Biol."},{"key":"232_CR87","doi-asserted-by":"crossref","first-page":"4265","DOI":"10.4049\/jimmunol.1500036","volume":"194","author":"A Metidji","year":"2015","unstructured":"Metidji, A. et al. IFN-\u03b1\/\u03b2 receptor signaling promotes regulatory T cell development and function under stress conditions. J. Immunol. 194, 4265\u20134276 (2015).","journal-title":"J. Immunol."},{"key":"232_CR88","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1016\/j.bbamcr.2011.01.034","volume":"1813","author":"J Scheller","year":"2011","unstructured":"Scheller, J., Chalaris, A., Schmidt-Arras, D. & Rose-John, S. The pro- and anti-inflammatory properties of the cytokine interleukin-6. Biochim. Biophys. Acta 1813, 878\u2013888 (2011).","journal-title":"Biochim. Biophys. Acta"},{"key":"232_CR89","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1042\/bj20030407","volume":"374","author":"PC Heinrich","year":"2003","unstructured":"Heinrich, P. C. et al. Principles of interleukin (IL)-6-type cytokine signalling and its regulation. Biochem. J. 374, 1\u201320 (2003).","journal-title":"Biochem. J."}],"container-title":["Nature Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42256-020-00232-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-020-00232-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-020-00232-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T22:07:27Z","timestamp":1733954847000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42256-020-00232-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,12]]},"references-count":89,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["232"],"URL":"https:\/\/doi.org\/10.1038\/s42256-020-00232-8","relation":{},"ISSN":["2522-5839"],"issn-type":[{"value":"2522-5839","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,12]]},"assertion":[{"value":"13 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 October 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}