{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T17:53:12Z","timestamp":1726422792087},"reference-count":78,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T00:00:00Z","timestamp":1631145600000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"Abstract<\/jats:title>\n Feature selection is crucial for the analysis of high-dimensional data, but benchmark studies for data with a survival outcome are rare. We compare 14 filter methods for feature selection based on 11 high-dimensional gene expression survival data sets. The aim is to provide guidance on the choice of filter methods for other researchers and practitioners. We analyze the accuracy of predictive models that employ the features selected by the filter methods. Also, we consider the run time, the number of selected features for fitting models with high predictive accuracy as well as the feature selection stability. We conclude that the simple variance filter outperforms all other considered filter methods. This filter selects the features with the largest variance and does not take into account the survival outcome. Also, we identify the correlation-adjusted regression scores filter as a more elaborate alternative that allows fitting models with similar predictive accuracy. Additionally, we investigate the filter methods based on feature rankings, finding groups of similar filters.<\/jats:p>","DOI":"10.1093\/bib\/bbab354","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T11:20:51Z","timestamp":1631100051000},"source":"Crossref","is-referenced-by-count":73,"title":["Benchmark of filter methods for feature selection in high-dimensional gene expression survival data"],"prefix":"10.1093","volume":"23","author":[{"given":"Andrea","family":"Bommert","sequence":"first","affiliation":[{"name":"Department of Statistics, TU Dortmund University, Vogelpothsweg 87, 44227, Dortmund, Germany"}]},{"given":"Thomas","family":"Welchowski","sequence":"additional","affiliation":[{"name":"Institute of Medical Biometry, Informatics and Epidemiology (IMBIE), Medical Faculty, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany"}]},{"given":"Matthias","family":"Schmid","sequence":"additional","affiliation":[{"name":"Institute of Medical Biometry, Informatics and Epidemiology (IMBIE), Medical Faculty, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany"}]},{"given":"J\u00f6rg","family":"Rahnenf\u00fchrer","sequence":"additional","affiliation":[{"name":"Department of Statistics, TU Dortmund University, Vogelpothsweg 87, 44227, Dortmund, Germany"}]}],"member":"286","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"key":"2022011920505506200_ref1","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon","year":"2003","journal-title":"Journal of Machine Learning Research"},{"issue":"4","key":"2022011920505506200_ref2","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1109\/TCBB.2012.33","article-title":"A survey on filter techniques for feature selection in gene eexpression microarray analysis","volume":"9","author":"Lazar","year":"2012","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"1\u20132","key":"2022011920505506200_ref3","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","article-title":"Wrappers for feature subset selection","volume":"97","author":"Kohavi","year":"1997","journal-title":"Artificial Intelligence"},{"key":"2022011920505506200_ref4","first-page":"41","volume-title":"Feature Set Search Algorithms","author":"Kittler","year":"1978"},{"issue":"3","key":"2022011920505506200_ref5","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1007\/s10489-017-0992-2","article-title":"Feature clustering based support vector machine recursive feature elimination for gene selection","volume":"48","author":"Huang","year":"2018","journal-title":"Applied Intelligence"},{"key":"2022011920505506200_ref6","first-page":"117","volume-title":"Feature Subset Selection Using a Genetic Algorithm","author":"Yang","year":"1998"},{"issue":"4","key":"2022011920505506200_ref7","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1109\/TEVC.2015.2504420","article-title":"Will N Browne, and Xin Yao. A survey on evolutionary computation approaches to feature selection","volume":"20","author":"Xue","year":"2016","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"9","key":"2022011920505506200_ref8","doi-asserted-by":"crossref","DOI":"10.3390\/app8091521","article-title":"Swarm intelligence algorithms for feature selection: A review","volume":"8","author":"Brezo\u010dnik","year":"2018","journal-title":"Applied Sciences"},{"issue":"4","key":"2022011920505506200_ref9","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1002\/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3","article-title":"The lasso method for variable selection in the Cox model","volume":"16","author":"Tibshirani","year":"1997","journal-title":"Stat Med"},{"issue":"3","key":"2022011920505506200_ref10","doi-asserted-by":"crossref","DOI":"10.1214\/08-AOAS169","article-title":"Random survival forests","volume":"2","author":"Ishwaran","year":"2008","journal-title":"The Annals of Applied Statistics"},{"issue":"5","key":"2022011920505506200_ref11","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Annals of Statistics"},{"issue":"4","key":"2022011920505506200_ref12","first-page":"477","article-title":"Boosting algorithms: Regularization, prediction and model fitting","volume":"22","author":"B\u00fchlmann","year":"2007","journal-title":"Statistical Science"},{"issue":"4","key":"2022011920505506200_ref13","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TKDE.2005.66","article-title":"Toward integrating feature selection algorithms for classification and clustering","volume":"17","author":"Liu","year":"2005","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"19","key":"2022011920505506200_ref14","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A review of feature selection techniques in bioinformatics","volume":"23","author":"Saeys","year":"2007","journal-title":"Bioinformatics"},{"issue":"1","key":"2022011920505506200_ref15","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","article-title":"A survey on feature selection methods","volume":"40","author":"Chandrashekar","year":"2014","journal-title":"Computers & Electrical Engineering"},{"key":"2022011920505506200_ref16","volume-title":"Feature Selection for Classification: A Review, pages 37\u201364","author":"Tang","year":"2014"},{"key":"2022011920505506200_ref17","first-page":"2015","article-title":"A review of feature selection and feature extraction methods applied on microarray data","author":"Hira","year":"2015","journal-title":"Advances in Bioinformatics"},{"key":"2022011920505506200_ref18","first-page":"1200","article-title":"A review of feature selection methods with applications","volume-title":"38th International Convention on Information and Communication Technology, Electronics and Microelectronics","author":"Jovi\u0107","year":"2015"},{"key":"2022011920505506200_ref19","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.neucom.2017.11.077","article-title":"Feature selection in machine learning: A new perspective","volume":"300","author":"Cai","year":"2018","journal-title":"Neurocomputing"},{"issue":"6","key":"2022011920505506200_ref20","doi-asserted-by":"crossref","DOI":"10.1145\/3136625","article-title":"Feature selection: A data perspective","volume":"50","author":"Li","year":"2018","journal-title":"ACM Computing Surveys"},{"issue":"1","key":"2022011920505506200_ref21","doi-asserted-by":"crossref","first-page":"3","DOI":"10.2478\/cait-2019-0001","article-title":"A review of feature selection and its methods","volume":"19","author":"Venkatesh","year":"2019","journal-title":"Cybernetics and Information Technologies"},{"key":"2022011920505506200_ref22","first-page":"51","article-title":"A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns","volume":"13","author":"Liu","year":"2002","journal-title":"Genome Inform"},{"issue":"3","key":"2022011920505506200_ref23","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s10115-012-0487-8","article-title":"A review of feature selection methods on synthetic data","volume":"34","author":"Bol\u00f3n-Canedo","year":"2013","journal-title":"Knowledge and Information Systems"},{"key":"2022011920505506200_ref24","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.ins.2014.05.042","article-title":"A review of microarray datasets and applied feature selection methods","volume":"282","author":"Bol\u00f3n-Canedo","year":"2014","journal-title":"Inform Sci"},{"issue":"2","key":"2022011920505506200_ref25","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.artmed.2004.01.007","article-title":"Filter versus wrapper gene selection approaches in dna microarray domains","volume":"31","author":"Inza","year":"2004","journal-title":"Artif Intell Med"},{"key":"2022011920505506200_ref26","first-page":"1289","article-title":"An extensive empirical study of feature selection metrics for text classification","volume":"3","author":"Forman","year":"2003","journal-title":"Journal of Machine Learning Research"},{"issue":"10","key":"2022011920505506200_ref27","doi-asserted-by":"crossref","first-page":"1964","DOI":"10.1002\/asi.23110","article-title":"A comprehensive empirical comparison of modern supervised classification and feature selection methods for text categorization","volume":"65","author":"Aphinyanaphongs","year":"2014","journal-title":"J Assoc Inf Sci Technol"},{"key":"2022011920505506200_ref28","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.procs.2017.12.046","article-title":"Performance evaluation of filter-based feature selection techniques in classifying portable executable files","volume":"125","author":"Darshan","year":"2018","journal-title":"Procedia Computer Science"},{"issue":"5","key":"2022011920505506200_ref29","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1021\/ci049875d","article-title":"A comparative study on feature selection methods for drug discovery","volume":"44","author":"Liu","year":"2004","journal-title":"J Chem Inf Comput Sci"},{"issue":"8","key":"2022011920505506200_ref30","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2022011920505506200_ref31","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3233\/IDA-1997-1302","article-title":"Feature selection for classification","volume":"1","author":"Dash","year":"1997","journal-title":"Intelligent Data Analysis"},{"key":"2022011920505506200_ref32","first-page":"178","article-title":"Filter methods for feature selection \u2013 a comparative study","author":"S\u00e1nchez-Maro\u00f1o","year":"2007","journal-title":"In International Conference on Intelligent Data Engineering and Automated Learning"},{"issue":"1","key":"2022011920505506200_ref33","first-page":"329","article-title":"Feature selection methods: Case of filter and wrapper approaches for maximising classification accuracy","volume":"26","author":"Wah","year":"2018","journal-title":"Pertanika Journal of Science & Technology"},{"issue":"2","key":"2022011920505506200_ref34","doi-asserted-by":"crossref","DOI":"10.1142\/S146902681550008X","article-title":"A comprehensive comparison on evolutionary feature selection approaches to classification","volume":"14","author":"Xue","year":"2015","journal-title":"International Journal of Computational Intelligence and Applications"},{"issue":"3","key":"2022011920505506200_ref35","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/JSTSP.2008.923858","article-title":"Information-theoretic feature selection in microarray data using variable complementarity","volume":"2","author":"Meyer","year":"2008","journal-title":"IEEE Journal of Selected Topics in Signal Processing"},{"key":"2022011920505506200_ref36","first-page":"27","article-title":"Conditional likelihood maximisation: A unifying framework for information theoretic feature selection","volume":"13","author":"Brown","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"2022011920505506200_ref37","volume-title":"Correlation-Based Feature Selection for Machine Learning","author":"Hall","year":"1999"},{"key":"2022011920505506200_ref38","doi-asserted-by":"crossref","DOI":"10.1016\/j.csda.2019.106839","article-title":"Benchmark for filter methods for feature selection in high-dimensional classification data","volume":"143","author":"Bommert","year":"2020","journal-title":"Computational Statistics & Data Analysis"},{"issue":"1","key":"2022011920505506200_ref39","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1080\/00949655.2014.929131","article-title":"Automatic model selection for high-dimensional survival analysis","volume":"85","author":"Lang","year":"2015","journal-title":"Journal of Statistical Computation and Simulation"},{"key":"2022011920505506200_ref40","doi-asserted-by":"crossref","DOI":"10.1155\/2017\/7907163","article-title":"A multicriteria approach to find predictive and sparse models with stable feature selection for high-dimensional data","volume":"2017","author":"Bommert","year":"2017","journal-title":"Comput Math Methods Med"},{"key":"2022011920505506200_ref41","volume-title":"Integration of Feature Selection Stability in Model Fitting","author":"Bommert","year":"2020"},{"key":"2022011920505506200_ref42","doi-asserted-by":"crossref","DOI":"10.21105\/joss.01903","article-title":"ref42: A modern object-oriented machine learning framework in ref67","author":"Lang","year":"2019","journal-title":"Journal of Open Source Software"},{"issue":"1","key":"2022011920505506200_ref43","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s10115-006-0040-8","article-title":"Stability of feature selection algorithms: A study on high-dimensional spaces","volume":"12","author":"Kalousis","year":"2007","journal-title":"Knowledge and Information Systems"},{"key":"2022011920505506200_ref44","doi-asserted-by":"crossref","volume-title":"Survival Analysis: Techniques for Censored and Truncated Data","author":"Klein","year":"2003","DOI":"10.1007\/b97377"},{"issue":"5","key":"2022011920505506200_ref45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v039.i05","article-title":"Regularization paths for Cox\u2019s proportional hazards model via coordinate descent","volume":"39","author":"Simon","year":"2011","journal-title":"J Stat Softw"},{"issue":"6","key":"2022011920505506200_ref46","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1002\/bimj.200610301","article-title":"Consistent estimation of the expected brier score in general survival models with right-censored event times","volume":"48","author":"Gerds","year":"2006","journal-title":"Biom J"},{"issue":"1","key":"2022011920505506200_ref47","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1093\/biomet\/77.1.147","article-title":"Martingale-based residuals for survival models","volume":"77","author":"Therneau","year":"1990","journal-title":"Biometrika"},{"key":"2022011920505506200_ref48","doi-asserted-by":"crossref","volume-title":"Unified Methods for Censored Longitudinal Data and Causality","author":"Van der Laan","year":"2003","DOI":"10.1007\/978-0-387-21700-0"},{"issue":"34","key":"2022011920505506200_ref49","first-page":"2194","article-title":"High-dimensional regression and variable selection using CAR scores","volume":"10","author":"Zuber","year":"2011","journal-title":"Stat Appl Genet Mol Biol"},{"issue":"4","key":"2022011920505506200_ref50","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1080\/00031305.2016.1277159","article-title":"Optimal whitening and decorrelation","volume":"72","author":"Kessy","year":"2018","journal-title":"The American Statistician"},{"issue":"1","key":"2022011920505506200_ref51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2202\/1544-6115.1175","article-title":"A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics","volume":"4","author":"Sch\u00e4fer","year":"2005","journal-title":"Stat Appl Genet Mol Biol"},{"issue":"13","key":"2022011920505506200_ref52","doi-asserted-by":"crossref","first-page":"2413","DOI":"10.1002\/sim.8116","article-title":"Correlation-adjusted regression survival scores for high-dimensional variable selection","volume":"38","author":"Welchowski","year":"2019","journal-title":"Stat Med"},{"issue":"4","key":"2022011920505506200_ref53","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4","article-title":"Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors","volume":"15","author":"Harrell","year":"1996","journal-title":"Stat Med"},{"key":"2022011920505506200_ref54","doi-asserted-by":"crossref","volume-title":"The elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"Hastie","year":"2009","DOI":"10.1007\/978-0-387-84858-7"},{"key":"2022011920505506200_ref55","volume-title":"praznik: Tools for Information-Based Feature Selection","author":"Kursa","year":"2020"},{"key":"2022011920505506200_ref56","first-page":"687","article-title":"Data visualization and feature selection: New algorithms for nongaussian data","volume-title":"Advances in Neural Information Processing Systems 12 (NIPS 1999)","author":"Yang","year":"1999"},{"issue":"22","key":"2022011920505506200_ref57","doi-asserted-by":"crossref","first-page":"8520","DOI":"10.1016\/j.eswa.2015.07.007","article-title":"Feature selection using joint mutual information maximisation","volume":"42","author":"Bennasar","year":"2015","journal-title":"Expert Systems with Applications"},{"key":"2022011920505506200_ref58","first-page":"91","article-title":"On the use of variable complementarity for feature selection in cancer classification","volume-title":"EvoWorkshops 2006: Applications of Evolutionary Computing","author":"Meyer","year":"2006"},{"key":"2022011920505506200_ref59","first-page":"1531","article-title":"Fast binary feature selection with conditional mutual information","volume":"5","author":"Fleuret","year":"2004","journal-title":"Journal of Machine Learning Research"},{"key":"2022011920505506200_ref60","volume-title":"mlr3filters: Filter Based Feature Selection for \u2018mlr3\u2019","author":"Schratz","year":"2020"},{"key":"2022011920505506200_ref61","doi-asserted-by":"crossref","volume-title":"Modeling Survival Data: Extending the Cox Model","author":"Therneau","year":"2000","DOI":"10.1007\/978-1-4757-3294-8"},{"key":"2022011920505506200_ref62","volume-title":"carSurv: Correlation-Adjusted Regression Survival (CARS) Scores","author":"Welchowski","year":"2018"},{"issue":"1","key":"2022011920505506200_ref63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v077.i01","article-title":"ref63: A fast implementation of random forests for high dimensional data in C++ and ref67","volume":"77","author":"Wright","year":"2017","journal-title":"J Stat Softw"},{"key":"2022011920505506200_ref64","volume-title":"xgboost: Extreme Gradient Boosting","author":"Chen","year":"2020"},{"key":"2022011920505506200_ref65","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/978-3-030-64583-0_19","article-title":"Adjusted measures for feature selection stability for data sets with similar features","volume-title":"Machine Learning, Optimization, and Data Science","author":"Bommert","year":"2020"},{"issue":"3","key":"2022011920505506200_ref66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbaa167","article-title":"Large-scale benchmark study of survival prediction methods using multi-omics data","volume":"22","author":"Herrmann","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022011920505506200_ref67","volume-title":"R: A Language and Environment for Statistical Computing","author":"R Core Team","year":"2020"},{"key":"2022011920505506200_ref68","volume-title":"mlr3proba: Probabilistic Supervised Learning for \u2018mlr3\u2019","author":"Sonabend","year":"2020"},{"key":"2022011920505506200_ref69","volume-title":"mlr3learners: Recommended Learners for \u2018mlr3\u2019","author":"Lang","year":"2020"},{"key":"2022011920505506200_ref70","volume-title":"mlr3pipelines: Preprocessing Operators and Pipelines for \u2018mlr3\u2019","author":"Binder","year":"2020"},{"issue":"5","key":"2022011920505506200_ref71","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v039.i05","article-title":"Regularization paths for Cox\u2019s proportional hazards model via coordinate descent","volume":"39","author":"Simon","year":"2011","journal-title":"J Stat Softw"},{"issue":"11","key":"2022011920505506200_ref72","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v064.i11","article-title":"BatchJobs and BatchExperiments: Abstraction mechanisms for using ref67 in batch environments","volume":"64","author":"Bischl","year":"2015","journal-title":"J Stat Softw"},{"issue":"59","key":"2022011920505506200_ref73","doi-asserted-by":"crossref","first-page":"3010","DOI":"10.21105\/joss.03010","article-title":"stabm: Stability measures for feature selection","volume":"6","author":"Bommert","year":"2021","journal-title":"Journal of Open Source Software"},{"key":"2022011920505506200_ref74","doi-asserted-by":"crossref","volume-title":"ggplot2: Elegant Graphics for Data Analysis","author":"Wickham","year":"2016","DOI":"10.1007\/978-3-319-24277-4"},{"key":"2022011920505506200_ref75","volume-title":"OrderedList: Similarities of Ordered Gene Lists","author":"Yang","year":"2020"},{"issue":"16","key":"2022011920505506200_ref76","doi-asserted-by":"crossref","first-page":"2080","DOI":"10.1093\/bioinformatics\/btm305","article-title":"Predicting survival from microarray data \u2013 a comparative study","volume":"23","author":"B\u00f8velstad","year":"2007","journal-title":"Bioinformatics"},{"issue":"2","key":"2022011920505506200_ref77","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1162\/EVCO_a_00069","article-title":"Resampling methods for meta-model validation with recommendations for evolutionary computation","volume":"20","author":"Bischl","year":"2012","journal-title":"Evol Comput"},{"key":"2022011920505506200_ref78","doi-asserted-by":"crossref","DOI":"10.1186\/1471-2105-10-11","article-title":"Filtering for increased power for microarray data analysis","volume":"10","author":"Hackstadt","year":"2009","journal-title":"BMC Bioinformatics"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab354\/42229629\/bbab354.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab354\/42229629\/bbab354.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,19]],"date-time":"2022-01-19T20:52:12Z","timestamp":1642625532000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab354\/6366322"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,8]]},"references-count":78,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1,17]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab354","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,1]]},"published":{"date-parts":[[2021,9,8]]}}}