{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:52:17Z","timestamp":1775325137467,"version":"3.50.1"},"reference-count":78,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2018,11,29]],"date-time":"2018-11-29T00:00:00Z","timestamp":1543449600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81872717"],"award-info":[{"award-number":["81872717"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81872715"],"award-info":[{"award-number":["81872715"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008240","name":"Department of Health of Hebei Province","doi-asserted-by":"publisher","award":["ZD2018022"],"award-info":[{"award-number":["ZD2018022"]}],"id":[{"id":"10.13039\/501100008240","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"DOI":"10.1093\/bib\/bby115","type":"journal-article","created":{"date-parts":[[2018,11,9]],"date-time":"2018-11-09T20:44:00Z","timestamp":1541796240000},"source":"Crossref","is-referenced-by-count":13,"title":["Multilevel heterogeneous omics data integration with kernel fusion"],"prefix":"10.1093","author":[{"given":"Haitao","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Health Statistics, School of Public Health, and Hebei Province Key Laboratory of Environment and Human Health, Hebei Medical University, Shijiazhuang, PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyan","family":"Cao","sequence":"additional","affiliation":[{"name":"Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"He","sequence":"additional","affiliation":[{"name":"Department of Mathematics, San Francisco State University, San Francisco, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Wang","sequence":"additional","affiliation":[{"name":"Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuehua","family":"Cui","sequence":"additional","affiliation":[{"name":"Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China"},{"name":"Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,11,29]]},"reference":[{"issue":"Suppl 2","key":"key\n\t\t\t\t2019012511123888300_ref1","doi-asserted-by":"crossref","first-page":"I1","DOI":"10.1186\/1752-0509-8-S2-I1","article-title":"Data integration in the era of omics: current and future challenges","volume":"8","author":"Gomez-Cabrero","year":"2014","journal-title":"BMC Syst Biol"},{"key":"key\n\t\t\t\t2019012511123888300_ref2","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1089\/omi.2015.0020","article-title":"The promise of multi-omics and clinical data integration to identify and target personalized healthcare approaches in autism spectrum disorders","volume":"19","author":"Higdon","year":"2015","journal-title":"OMICS"},{"key":"key\n\t\t\t\t2019012511123888300_ref3","doi-asserted-by":"crossref","first-page":"13090","DOI":"10.1038\/ncomms13090","article-title":"Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli","volume":"7","author":"Kim","year":"2016","journal-title":"Nat Commun"},{"key":"key\n\t\t\t\t2019012511123888300_ref4","first-page":"415","volume-title":"Methods Mol Biol","author":"Tieri","year":"2011"},{"key":"key\n\t\t\t\t2019012511123888300_ref5","doi-asserted-by":"crossref","first-page":"84","DOI":"10.3389\/fgene.2017.00084","article-title":"More is better: recent progress in multi-omics data integration methods","volume":"8","author":"Huang","year":"2017","journal-title":"Front Genet"},{"key":"key\n\t\t\t\t2019012511123888300_ref6","doi-asserted-by":"crossref","first-page":"1984","DOI":"10.1109\/TIFS.2016.2569061","article-title":"Discriminant correlation analysis: real-time feature level fusion for multimodal biometric recognition","volume":"11","author":"Haghighat","year":"2016","journal-title":"IEEE Trans Inf Forensics and Security"},{"key":"key\n\t\t\t\t2019012511123888300_ref7","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1109\/LSP.2013.2295054","article-title":"Decision fusion with unknown sensor detection probability","volume":"21","author":"Ciuonzo","year":"2014","journal-title":"IEEE Signal Process Lett"},{"key":"key\n\t\t\t\t2019012511123888300_ref8","article-title":"Methods of genomic data fusion: An overview","author":"Tretyakov","year":"2006"},{"key":"key\n\t\t\t\t2019012511123888300_ref9","article-title":"Data fusion lexicon","author":"White","year":"1986"},{"key":"key\n\t\t\t\t2019012511123888300_ref10","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/5.554205","article-title":"An introduction to multisensor data fusion","volume":"85","author":"Hall","year":"1997","journal-title":"Proc IEEE"},{"issue":"6","key":"key\n\t\t\t\t2019012511123888300_ref11","doi-asserted-by":"crossref","first-page":"704504","DOI":"10.1155\/2013\/704504","article-title":"A review of data fusion techniques","volume":"2013","author":"Castanedo","year":"2013","journal-title":"Scientific World Journal"},{"key":"key\n\t\t\t\t2019012511123888300_ref12","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1177\/027836498800700608","article-title":"Sensor models and multisensor integration","volume":"7","author":"Durrant-Whyte","year":"1988","journal-title":"Int J Rob Res"},{"key":"key\n\t\t\t\t2019012511123888300_ref13","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/5.554206","article-title":"Sensor fusion potential exploitation-innovative architectures and illustrative applications","volume":"85","author":"Dasarathy","year":"1997","journal-title":"Proc IEEE"},{"key":"key\n\t\t\t\t2019012511123888300_ref14","author":"Yu","year":"2009"},{"key":"key\n\t\t\t\t2019012511123888300_ref15","volume-title":"An Introduction to Support Vector Machines","author":"Cristianini","year":"2000"},{"key":"key\n\t\t\t\t2019012511123888300_ref16","volume-title":"Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond","author":"Sch\u00f6lkopf","year":"2002"},{"key":"key\n\t\t\t\t2019012511123888300_ref17","volume-title":"Statistical Learning Theory","author":"Vapnik","year":"1998"},{"key":"key\n\t\t\t\t2019012511123888300_ref18","volume-title":"The Nature of Statistical Learning Theory","author":"Vapnik","year":"2013"},{"key":"key\n\t\t\t\t2019012511123888300_ref19","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/978-3-319-21852-6_3","volume-title":"Measures of Complexity","author":"Vapnik","year":"2015"},{"key":"key\n\t\t\t\t2019012511123888300_ref20","first-page":"591","article-title":"Predicting disease trait with genomic data: a composite kernel approach","volume":"18","author":"Yang","year":"2017","journal-title":"Brief Bioinform"},{"key":"key\n\t\t\t\t2019012511123888300_ref21","first-page":"2785","article-title":"Improved SVM regression using mixtures of kernels","volume-title":"Proceedings of the 2002 International Joint Conference on Neural Networks, 2002","author":"Smits","year":"2002"},{"key":"key\n\t\t\t\t2019012511123888300_ref22","first-page":"144","volume-title":"Proceedings of the Fifth Annual Workshop on Computational Learning Theory","author":"Boser"},{"key":"key\n\t\t\t\t2019012511123888300_ref23","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1038\/nrc3721","article-title":"Principles and methods of integrative genomic analyses in cancer","volume":"14","author":"Kristensen","year":"2014","journal-title":"Nat Rev Cancer"},{"key":"key\n\t\t\t\t2019012511123888300_ref24","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4057.003.0015","article-title":"1 kernel-based integration of genomic data using semidefinite programming","volume-title":"Kernel Methods in Computational Biology","author":"Lanckriet","year":"2004"},{"key":"key\n\t\t\t\t2019012511123888300_ref25","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1159\/000312641","article-title":"Genomic similarity and kernel methods I: advancements by building on mathematical and statistical foundations","volume":"70","author":"Schaid","year":"2010","journal-title":"Hum Hered"},{"key":"key\n\t\t\t\t2019012511123888300_ref26","doi-asserted-by":"crossref","DOI":"10.1109\/BIBM.2015.7359908","article-title":"Gene prioritization through geometric-inspired kernel data fusion","volume-title":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Zakeri"},{"key":"key\n\t\t\t\t2019012511123888300_ref27","first-page":"2491","article-title":"SimpleMKL","volume":"9","author":"Rakotomamonjy","year":"2008","journal-title":"J Mach Learn Res"},{"issue":"13","key":"key\n\t\t\t\t2019012511123888300_ref28","doi-asserted-by":"crossref","first-page":"1850","DOI":"10.1093\/bioinformatics\/btu118","article-title":"Protein fold recognition using geometric kernel data fusion","volume":"30","author":"Zakeri","year":"2014","journal-title":"Bioinformatics"},{"key":"key\n\t\t\t\t2019012511123888300_ref29","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1137\/050637996","article-title":"Geometric means in a novel vector space structure on symmetric positive-definite matrices","volume":"29","author":"Arsigny","year":"2007","journal-title":"SIAM J Matrix Anal and Appl"},{"key":"key\n\t\t\t\t2019012511123888300_ref30","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1186\/1471-2105-11-309","article-title":"L2-norm multiple kernel learning and its application to biomedical data fusion","volume":"11","author":"Yu","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"key\n\t\t\t\t2019012511123888300_ref31","first-page":"2211","article-title":"Multiple kernel learning algorithms","volume":"12","author":"G\u00f6nen","year":"2011","journal-title":"J Mach Learn Res"},{"key":"key\n\t\t\t\t2019012511123888300_ref32","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1093\/bioinformatics\/btn112","article-title":"Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection","volume":"24","author":"Damoulas","year":"2008","journal-title":"Bioinformatics"},{"key":"key\n\t\t\t\t2019012511123888300_ref33","first-page":"129","volume-title":"Unsupervised multiple kernel learning","author":"Zhuang"},{"key":"key\n\t\t\t\t2019012511123888300_ref34","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.neucom.2014.11.078","article-title":"EasyMKL: a scalable multiple kernel learning algorithm","volume":"169","author":"Aiolli","year":"2015","journal-title":"Neurocomputing"},{"key":"key\n\t\t\t\t2019012511123888300_ref35","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1093\/bioinformatics\/bth294","article-title":"A statistical framework for genomic data fusion","volume":"20","author":"Lanckriet","year":"2004","journal-title":"Bioinformatics"},{"key":"key\n\t\t\t\t2019012511123888300_ref36","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1002\/gepi.21676","article-title":"SNP set association analysis for familial data","volume":"36","author":"Schifano","year":"2012","journal-title":"Genet Epidemiol"},{"key":"key\n\t\t\t\t2019012511123888300_ref37","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1089\/omi.2006.10.40","article-title":"Diffusion kernel-based logistic regression models for protein function prediction","volume":"10","author":"Lee","year":"2006","journal-title":"OMICS"},{"key":"key\n\t\t\t\t2019012511123888300_ref38","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1002\/dta.275","article-title":"Application of genetic algorithm-kernel partial least square as a novel non-linear feature selection method: partitioning of drug molecules","volume":"5","author":"Noorizadeh","year":"2013","journal-title":"Drug Test Anal"},{"key":"key\n\t\t\t\t2019012511123888300_ref39","first-page":"97","article-title":"Kernel partial least squares regression in reproducing kernel hilbert space","volume":"2","author":"Rosipal","year":"2002","journal-title":"J Mach Learn Res"},{"key":"key\n\t\t\t\t2019012511123888300_ref40","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.chemolab.2005.03.003","article-title":"A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction","volume":"79","author":"Kim","year":"2005","journal-title":"Chemometr Intell Lab Syst"},{"key":"key\n\t\t\t\t2019012511123888300_ref41","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1002\/minf.201000061","article-title":"Best practices for QSAR model development, validation, and exploitation","volume":"29","author":"Tropsha","year":"2010","journal-title":"Mol Inform"},{"key":"key\n\t\t\t\t2019012511123888300_ref42","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1002\/cem.1180080204","article-title":"A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: theory and algorithm","volume":"8","author":"R\u00e4nnar","year":"1994","journal-title":"J Chemom"},{"key":"key\n\t\t\t\t2019012511123888300_ref43","doi-asserted-by":"crossref","first-page":"2072","DOI":"10.1093\/bioinformatics\/btg283","article-title":"Linear regression and two-class classification with gene expression data","volume":"19","author":"Huang","year":"2003","journal-title":"Bioinformatics"},{"key":"key\n\t\t\t\t2019012511123888300_ref44","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1002\/bimj.200410135","article-title":"Estimation of the Youden Index and its associated cutoff point","volume":"47","author":"Fluss","year":"2005","journal-title":"Biom J"},{"key":"key\n\t\t\t\t2019012511123888300_ref45","first-page":"71471J-71471J-71479","volume-title":"Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images","author":"Zhuo"},{"key":"key\n\t\t\t\t2019012511123888300_ref46","volume-title":"Practical Handbook of Genetic Algorithms: Complex Coding Systems","author":"Chambers","year":"1998"},{"key":"key\n\t\t\t\t2019012511123888300_ref47","volume-title":"Genetic Algorithms in Search, Optimization, and Machine Learning","author":"Golberg","year":"1989"},{"key":"key\n\t\t\t\t2019012511123888300_ref48","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1002\/cem.1180060506","article-title":"Genetic algorithms as a strategy for feature selection","volume":"6","author":"Leardi","year":"1992","journal-title":"J Chemom"},{"key":"key\n\t\t\t\t2019012511123888300_ref49","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/978-1-4684-8941-5_21","volume-title":"Adaptive Control of Ill-Defined Systems","author":"Holland","year":"1984"},{"key":"key\n\t\t\t\t2019012511123888300_ref50","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1016\/j.asoc.2010.06.017","article-title":"Genetic algorithm with adaptive elitist-population strategies for multimodal function optimization","volume":"11","author":"Liang","year":"2011","journal-title":"Appl Soft Comput"},{"key":"key\n\t\t\t\t2019012511123888300_ref51","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1002\/1099-128X(200009\/12)14:5\/6<643::AID-CEM621>3.0.CO;2-E","article-title":"Application of genetic algorithm-PLS for feature selection in spectral data sets","volume":"14","author":"Leardi","year":"2000","journal-title":"J Chemom"},{"key":"key\n\t\t\t\t2019012511123888300_ref52","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1038\/nmeth.2956","article-title":"TCGA-assembler: open-source software for retrieving and processing TCGA data","volume":"11","author":"Zhu","year":"2014","journal-title":"Nat Methods"},{"key":"key\n\t\t\t\t2019012511123888300_ref53","first-page":"1233","article-title":"Understanding and treating triple-negative breast cancer","volume":"22","author":"Anders","year":"2008","journal-title":"Oncology (Williston Park)"},{"key":"key\n\t\t\t\t2019012511123888300_ref54","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1158\/1078-0432.CCR-07-1658","article-title":"Basal-like breast cancer defined by five biomarkers has superior prognostic value than triple-negative phenotype","volume":"14","author":"Cheang","year":"2008","journal-title":"Clin Cancer Res"},{"key":"key\n\t\t\t\t2019012511123888300_ref55","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1097\/PPO.0b013e3181cf04be","article-title":"What is the difference between triple-negative and basal breast cancers?","volume":"16","author":"Seal","year":"2010","journal-title":"Cancer J"},{"key":"key\n\t\t\t\t2019012511123888300_ref56","doi-asserted-by":"crossref","first-page":"4429","DOI":"10.1158\/1078-0432.CCR-06-3045","article-title":"Triple-negative breast cancer: clinical features and patterns of recurrence","volume":"13","author":"Dent","year":"2007","journal-title":"Clin Cancer Res"},{"key":"key\n\t\t\t\t2019012511123888300_ref57","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1056\/NEJMoa052933","article-title":"Concordance among gene-expression-based predictors for breast cancer","volume":"355","author":"Fan","year":"2006","journal-title":"N Engl J Med"},{"key":"key\n\t\t\t\t2019012511123888300_ref58","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.1200\/JCO.2009.24.9284","article-title":"Breast cancer subtypes and the risk of local and regional relapse","volume":"28","author":"Voduc","year":"2010","journal-title":"J Clin Oncol"},{"key":"key\n\t\t\t\t2019012511123888300_ref59","volume-title":"UpToDate","author":"Foukakis","year":"2013"},{"key":"key\n\t\t\t\t2019012511123888300_ref60","doi-asserted-by":"crossref","DOI":"10.1093\/jnci\/dju055","article-title":"US incidence of breast cancer subtypes defined by joint hormone receptor and HER2 status","volume":"106","author":"Howlader","year":"2014","journal-title":"J Natl Cancer Inst"},{"key":"key\n\t\t\t\t2019012511123888300_ref61","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1038\/nature11412","article-title":"Comprehensive molecular portraits of human breast tumours","volume":"490","author":"Network","year":"2012","journal-title":"Nature"},{"key":"key\n\t\t\t\t2019012511123888300_ref62","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1093\/bioinformatics\/btt610","article-title":"A pathway-based data integration framework for prediction of disease progression","volume":"30","author":"Seoane","year":"2013","journal-title":"Bioinformatics"},{"key":"key\n\t\t\t\t2019012511123888300_ref63","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1111\/j.1467-9868.2010.00740.x","article-title":"Stability selection","volume":"72","author":"Meinshausen","year":"2010","journal-title":"J R Stat Soc Series B Stat Methodol"},{"key":"key\n\t\t\t\t2019012511123888300_ref64","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/0005-2795(75)90109-9","article-title":"Comparison of the predicted and observed secondary structure of T4 phage lysozyme","volume":"405","author":"Matthews","year":"1975","journal-title":"Biochim Biophys Acta"},{"key":"key\n\t\t\t\t2019012511123888300_ref65","article-title":"Learning the kernel matrix via predictive low-rank approximations","author":"Stra\u017ear","year":"2016"},{"key":"key\n\t\t\t\t2019012511123888300_ref66","article-title":"Random features for large-scale kernel machines","author":"Rahimi","year":"2007","journal-title":"Advances in Neural Information Processing Systems"},{"key":"key\n\t\t\t\t2019012511123888300_ref67","article-title":"A la carte-learning fast kernels","volume-title":"Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS), 2015,","author":"Yang"},{"key":"key\n\t\t\t\t2019012511123888300_ref68","first-page":"701","volume-title":"Proceedings of The 31st International Conference on Machine Learning","author":"Si"},{"key":"key\n\t\t\t\t2019012511123888300_ref69","volume-title":"Fastfood\u2014Approximating Kernel Expansions in Loglinear Time","author":"Szab\u00f3","year":"2013"},{"key":"key\n\t\t\t\t2019012511123888300_ref70","first-page":"1648","volume-title":"Advances in Neural Information Processing Systems","author":"Rudi","year":"2015"},{"key":"key\n\t\t\t\t2019012511123888300_ref71","first-page":"3115","article-title":"Nystrom approximation for sparse kernel methods: theoretical analysis and empirical evaluation","volume-title":"AAAI'15 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence","author":"Xu","year":"2015"},{"key":"key\n\t\t\t\t2019012511123888300_ref72","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1109\/TNNLS.2014.2359798","article-title":"Large-scale nystr\u00f6m kernel matrix approximation using randomized SVD","volume":"26","author":"Li","year":"2015","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"key\n\t\t\t\t2019012511123888300_ref73","first-page":"28","volume-title":"30th International Conference on Machine Learning","author":"Gittens","year":"2013"},{"key":"key\n\t\t\t\t2019012511123888300_ref74","first-page":"682","article-title":"Using the Nystr\u00f6m method to speed up kernel machines","volume-title":"Proceedings of the 14th Annual Conference on Neural Information Processing Systems, 2001","author":"Williams"},{"key":"key\n\t\t\t\t2019012511123888300_ref75","first-page":"243","article-title":"Efficient SVM training using low-rank kernel representations","volume":"2","author":"Fine","year":"2002","journal-title":"J Mach Learn Res"},{"key":"key\n\t\t\t\t2019012511123888300_ref76","first-page":"33","volume-title":"Proceedings of the 22nd International Conference on Machine Learning","author":"Bach"},{"key":"key\n\t\t\t\t2019012511123888300_ref77","first-page":"341","article-title":"Low-rank kernel learning with Bregman matrix divergences","volume":"10","author":"Kulis","year":"2009","journal-title":"J Mach Learn Res"},{"key":"key\n\t\t\t\t2019012511123888300_ref78","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1038\/nature16166","article-title":"Substantial contribution of extrinsic risk factors to cancer development","volume":"529","author":"Wu","year":"2016","journal-title":"Nature"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bib\/advance-article-pdf\/doi\/10.1093\/bib\/bby115\/26864263\/bby115.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T06:56:06Z","timestamp":1720767366000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/advance-article\/doi\/10.1093\/bib\/bby115\/5200557"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,29]]},"references-count":78,"URL":"https:\/\/doi.org\/10.1093\/bib\/bby115","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,29]]}}}