{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T09:04:31Z","timestamp":1773047071720,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2017,4,24]],"date-time":"2017-04-24T00:00:00Z","timestamp":1492992000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2017,4,24]],"date-time":"2017-04-24T00:00:00Z","timestamp":1492992000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"crossref","award":["AI116794"],"award-info":[{"award-number":["AI116794"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000038","name":"U.S. Food and Drug Administration","doi-asserted-by":"crossref","award":["E7295"],"award-info":[{"award-number":["E7295"]}],"id":[{"id":"10.13039\/100000038","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000038","name":"U.S. Food and Drug Administration","doi-asserted-by":"crossref","award":["E7519"],"award-info":[{"award-number":["E7519"]}],"id":[{"id":"10.13039\/100000038","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100006229","name":"Oak Ridge Institute for Science and Education","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006229","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["LM009012"],"award-info":[{"award-number":["LM009012"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["DK112217"],"award-info":[{"award-number":["DK112217"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BioData Mining"],"published-print":{"date-parts":[[2017,12]]},"DOI":"10.1186\/s13040-017-0134-8","type":"journal-article","created":{"date-parts":[[2017,4,24]],"date-time":"2017-04-24T13:22:22Z","timestamp":1493040142000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discovery"],"prefix":"10.1186","volume":"10","author":[{"given":"Nathaniel M.","family":"Crabtree","sequence":"first","affiliation":[]},{"given":"Jason H.","family":"Moore","sequence":"additional","affiliation":[]},{"given":"John F.","family":"Bowyer","sequence":"additional","affiliation":[]},{"given":"Nysia I.","family":"George","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,4,24]]},"reference":[{"key":"134_CR1","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-1-4614-6846-2_7","volume-title":"Genetic Programming Theory and Practice X","author":"J Moore","year":"2013","unstructured":"Moore J, Hill D, Sulovari A, Kidd L. Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-processing. In: Riolo R, Vladislavleva E, Ritchie MD, Moore JH, editors. Genetic Programming Theory and Practice X. New York: Springer; 2013. p. 87\u2013101. Genetic and Evolutionary Computation."},{"key":"134_CR2","doi-asserted-by":"crossref","unstructured":"Bottou L, Cortes C, Denker JS, Drucker H, Guyon I, Jackel LD, LeCun Y, Muller UA, Sackinger E, Simard P. Comparison of classifier methods: a case study in handwritten digit recognition. In Pattern Recognition, 1994 Vol 2-Conference B: Computer Vision & Image Processing, Proceedings of the 12th IAPR International Conference on. IEEE; 1994. pp. 77\u201382.","DOI":"10.1109\/ICPR.1994.576879"},{"key":"134_CR3","doi-asserted-by":"crossref","unstructured":"Knerr S, Personnaz L, Dreyfus G. Single-layer learning revisited: a stepwise procedure for building and training a neural network. In Neurocomputing. New York: Springer; 1990. pp. 41\u201350.","DOI":"10.1007\/978-3-642-76153-9_5"},{"key":"134_CR4","doi-asserted-by":"publisher","first-page":"2429","DOI":"10.1093\/bioinformatics\/bth267","volume":"20","author":"T Li","year":"2004","unstructured":"Li T, Zhang C, Ogihara M. A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics. 2004;20:2429\u201337.","journal-title":"Bioinformatics"},{"key":"134_CR5","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","volume":"7","author":"RA Fisher","year":"1936","unstructured":"Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugen. 1936;7:179\u201388.","journal-title":"Ann Eugen"},{"key":"134_CR6","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1002\/gepi.1117","volume":"23","author":"JH Moore","year":"2002","unstructured":"Moore JH, Parker JS, Olsen NJ, Aune TM. Symbolic discriminant analysis of microarray data in autoimmune disease. Genet Epidemiol. 2002;23:57\u201369.","journal-title":"Genet Epidemiol"},{"key":"134_CR7","first-page":"2292","volume-title":"Evolutionary Computation, 2003 CEC '03 The 2003 Congress on; 8-12 Dec. 2003","author":"S Dipti","year":"2003","unstructured":"Dipti S, Seow TH. Particle swarm inspired evolutionary algorithm (PS-EA) for multiobjective optimization problems. In: Evolutionary Computation, 2003 CEC '03 The 2003 Congress on; 8-12 Dec. 2003, vol. 2294. 2003. p. 2292\u20137."},{"key":"134_CR8","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1159\/000099184","volume":"63","author":"JH Moore","year":"2007","unstructured":"Moore JH, Barney N, Tsai CT, Chiang FT, Gui J, White BC. Symbolic modeling of epistasis. Hum Hered. 2007;63:120\u201333.","journal-title":"Hum Hered"},{"key":"134_CR9","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/ICEC.1994.350037","volume-title":"Evolutionary Computation, 1994 IEEE World Congress on Computational Intelligence, Proceedings of the First IEEE Conference on; 27-29 Jun 1994","author":"J Horn","year":"1994","unstructured":"Horn J, Nafpliotis N, Goldberg DE. A niched Pareto genetic algorithm for multiobjective optimization. In: Evolutionary Computation, 1994 IEEE World Congress on Computational Intelligence, Proceedings of the First IEEE Conference on; 27-29 Jun 1994, vol. 81. 1994. p. 82\u20137."},{"key":"134_CR10","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random Forests. Mach Learn. 2001;45:5\u201332.","journal-title":"Mach Learn"},{"key":"134_CR11","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1186\/1471-2105-8-328","volume":"8","author":"R Diaz-Uriarte","year":"2007","unstructured":"Diaz-Uriarte R. GeneSrF and varSelRF: a web-based tool and R package for gene selection and classification using random forest. BMC Bioinformatics. 2007;8:328.","journal-title":"BMC Bioinformatics"},{"key":"134_CR12","doi-asserted-by":"publisher","first-page":"e107801","DOI":"10.1371\/journal.pone.0107801","volume":"9","author":"V Fortino","year":"2014","unstructured":"Fortino V, Kinaret P, Fyhrquist N, Alenius H, Greco D. A robust and accurate method for feature selection and prioritization from multi-class OMICs data. PLoS One. 2014;9:e107801.","journal-title":"PLoS One"},{"key":"134_CR13","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inf Theory. 1967;13:21\u20137.","journal-title":"IEEE Trans Inf Theory"},{"key":"134_CR14","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1186\/1471-2105-12-450","volume":"12","author":"S Li","year":"2011","unstructured":"Li S, Harner EJ, Adjeroh DA. Random KNN feature selection-a fast and stable alternative to Random Forests. BMC Bioinformatics. 2011;12:450.","journal-title":"BMC Bioinformatics"},{"key":"134_CR15","volume-title":"Statistical learning theory","author":"VN Vapnik","year":"1998","unstructured":"Vapnik VN, Vapnik V. Statistical learning theory. New York: Wiley; 1998."},{"key":"134_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-14-307","volume":"14","author":"N Ghaffari","year":"2013","unstructured":"Ghaffari N, Yousefi MR, Johnson CD, Ivanov I, Dougherty ER. Modeling the next generation sequencing sample processing pipeline for the purposes of classification. BMC Bioinformatics. 2013;14:1.","journal-title":"BMC Bioinformatics"},{"key":"134_CR17","unstructured":"Determan C. Optimal algorithm for metabolomics classification and feature selection varies by dataset. Int J Biol. 2015;7:100."},{"key":"134_CR18","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1186\/1471-2105-9-319","volume":"9","author":"A Statnikov","year":"2008","unstructured":"Statnikov A, Wang L, Aliferis CF. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics. 2008;9:319.","journal-title":"BMC Bioinformatics"},{"key":"134_CR19","doi-asserted-by":"publisher","first-page":"D760","DOI":"10.1093\/nar\/gkl887","volume":"35","author":"T Barrett","year":"2007","unstructured":"Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Edgar R. NCBI GEO: mining tens of millions of expression profiles--database and tools update. Nucleic Acids Res. 2007;35:D760\u2013765.","journal-title":"Nucleic Acids Res"},{"key":"134_CR20","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.neuro.2013.04.003","volume":"37","author":"MS Levi","year":"2013","unstructured":"Levi MS, Patton RE, Hanig JP, Tranter KM, George NI, James LP, Davis KJ, Bowyer JF. Serum myoglobin, but not lipopolysaccharides, is predictive of AMPH-induced striatal neurotoxicity. Neurotoxicology. 2013;37:40\u201350.","journal-title":"Neurotoxicology"},{"key":"134_CR21","doi-asserted-by":"publisher","first-page":"R130","DOI":"10.1186\/gb-2009-10-11-r130","volume":"10","author":"C Wu","year":"2009","unstructured":"Wu C, Orozco C, Boyer J, Leglise M, Goodale J, Batalov S, Hodge CL, Haase J, Janes J, Huss 3rd JW, Su AI. BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol. 2009;10:R130.","journal-title":"Genome Biol"},{"key":"134_CR22","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1038\/nature12531","volume":"501","author":"T Lappalainen","year":"2013","unstructured":"Lappalainen T, Sammeth M, Friedlander MR, t Hoen PA, Monlong J, Rivas MA, Gonzalez-Porta M, Kurbatova N, Griebel T, Ferreira PG, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013;501:506\u201311.","journal-title":"Nature"},{"key":"134_CR23","unstructured":"Carey V. geuvPack: summarized experiment with expression data from GEUVADIS., R package version 1.7.2 edition; 2016."},{"key":"134_CR24","doi-asserted-by":"crossref","unstructured":"Zararsiz G, Goksuluk D, Korkmaz S, Eldem V, Duru IP, Ozturk A, Unver T. Classification of RNA-Seq Data via Bagging Support Vector Machines. bioRxiv. 2014:007526.","DOI":"10.1101\/007526"},{"issue":"12","key":"134_CR25","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","volume":"15","author":"MI Love","year":"2014","unstructured":"Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.","journal-title":"Genome Biol"},{"key":"134_CR26","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s10115-006-0040-8","volume":"12","author":"A Kalousis","year":"2007","unstructured":"Kalousis A, Prados J, Hilario M. Stability of feature selection algorithms: a study on high-dimensional spaces. Knowl Inf Syst. 2007;12:95\u2013116.","journal-title":"Knowl Inf Syst"},{"key":"134_CR27","unstructured":"Kuhn M. Building predictive models in R using the caret package. Nucleic Acids Res. 2008;28:26."},{"key":"134_CR28","doi-asserted-by":"publisher","first-page":"D67","DOI":"10.1093\/nar\/gkv1276","volume":"44","author":"K Clark","year":"2016","unstructured":"Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. GenBank. Nucleic Acids Res. 2016;44:D67\u201372.","journal-title":"Nucleic Acids Res"},{"issue":"Suppl 1","key":"134_CR29","doi-asserted-by":"publisher","first-page":"S3","DOI":"10.1186\/1755-8794-6-S1-S3","volume":"6","author":"H Wang","year":"2013","unstructured":"Wang H, Zhang H, Dai Z, Chen MS, Yuan Z. TSG: a new algorithm for binary and multi-class cancer classification and informative genes selection. BMC Med Genomics. 2013;6 Suppl 1:S3.","journal-title":"BMC Med Genomics"},{"key":"134_CR30","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/11573067_20","volume-title":"Biological and Medical Data Analysis: 6th International Symposium, ISBMDA 2005, Aveiro, Portugal, November 10-11, 2005 Proceedings","author":"M Sordo","year":"2005","unstructured":"Sordo M, Zeng Q. On Sample Size and Classification Accuracy: A Performance Comparison. In: Oliveira JL, Maojo V, Mart\u00edn-S\u00e1nchez F, Pereira AS, editors. Biological and Medical Data Analysis: 6th International Symposium, ISBMDA 2005, Aveiro, Portugal, November 10-11, 2005 Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg; 2005. p. 193\u2013201."},{"key":"134_CR31","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf B, Smola AJ. Learning with kernels: support vector machines, regularization, optimization, and beyond. Cambridge: MIT press; 2002.","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"134_CR32","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1109\/TEVC.2005.856205","volume":"9","author":"DH Wolpert","year":"2005","unstructured":"Wolpert DH, Macready WG. Coevolutionary free lunches. Evol Comput IEEE Trans. 2005;9:721\u201335.","journal-title":"Evol Comput IEEE Trans"},{"key":"134_CR33","first-page":"3","volume-title":"Proceedings of the Second European Workshop on Data Mining and Text Mining in Bioinformatics","author":"H Chai","year":"2004","unstructured":"Chai H, Domeniconi C. An evaluation of gene selection methods for multi-class microarray data classification. In: Proceedings of the Second European Workshop on Data Mining and Text Mining in Bioinformatics. 2004. p. 3\u201310."},{"key":"134_CR34","doi-asserted-by":"publisher","first-page":"i86","DOI":"10.1093\/bioinformatics\/btn145","volume":"24","author":"J Liu","year":"2008","unstructured":"Liu J, Ranka S, Kahveci T. Classification and feature selection algorithms for multi-class CGH data. Bioinformatics. 2008;24:i86\u201395.","journal-title":"Bioinformatics"},{"key":"134_CR35","doi-asserted-by":"publisher","first-page":"e0133315","DOI":"10.1371\/journal.pone.0133315","volume":"10","author":"JF Bowyer","year":"2015","unstructured":"Bowyer JF, Tranter KM, Hanig JP, Crabtree NM, Schleimer RP, George NI. Evaluating the stability of RNA-Seq transcriptome profiles and drug-induced immune-related expression changes in whole blood. PLoS One. 2015;10:e0133315.","journal-title":"PLoS One"},{"key":"134_CR36","first-page":"245","volume":"2013","author":"A Jeni L\u00e1\u00f3","year":"2013","unstructured":"Jeni L\u00e1\u00f3 A, Cohn JF, De La Torre F. Facing imbalanced data recommendations for the use of performance metrics. Int Conf Affect Comput Intell Interact Workshops. 2013;2013:245\u201351.","journal-title":"Int Conf Affect Comput Intell Interact Workshops"},{"key":"134_CR37","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1080\/01635580802033110","volume":"60","author":"HC Erichsen","year":"2008","unstructured":"Erichsen HC, Peters U, Eck P, Welch R, Schoen RE, Yeager M, Levine M, Hayes RB, Chanock S. Genetic variation in sodium-dependent vitamin C transporters SLC23A1 and SLC23A2 and risk of advanced colorectal adenoma. Nutr Cancer. 2008;60:652\u20139.","journal-title":"Nutr Cancer"},{"key":"134_CR38","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/s12263-013-0346-6","volume":"8","author":"EJ Duell","year":"2013","unstructured":"Duell EJ, Lujan-Barroso L, Llivina C, Munoz X, Jenab M, Boutron-Ruault MC, Clavel-Chapelon F, Racine A, Boeing H, Buijsse B, et al. Vitamin C transporter gene (SLC23A1 and SLC23A2) polymorphisms, plasma vitamin C levels, and gastric cancer risk in the EPIC cohort. Genes Nutr. 2013;8:549\u201360.","journal-title":"Genes Nutr"},{"key":"134_CR39","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1126\/science.1246886","volume":"343","author":"CG Joseph","year":"2014","unstructured":"Joseph CG, Darrah E, Shah AA, Skora AD, Casciola-Rosen LA, Wigley FM, Boin F, Fava A, Thoburn C, Kinde I, et al. Association of the autoimmune disease scleroderma with an immunologic response to cancer. Science. 2014;343:152\u20137.","journal-title":"Science"},{"key":"134_CR40","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1038\/nchembio.1313","volume":"9","author":"P Magnaghi","year":"2013","unstructured":"Magnaghi P, D'Alessio R, Valsasina B, Avanzi N, Rizzi S, Asa D, Gasparri F, Cozzi L, Cucchi U, Orrenius C, et al. Covalent and allosteric inhibitors of the ATPase VCP\/p97 induce cancer cell death. Nat Chem Biol. 2013;9:548\u201356.","journal-title":"Nat Chem Biol"},{"key":"134_CR41","doi-asserted-by":"publisher","first-page":"4392","DOI":"10.18632\/oncotarget.2025","volume":"5","author":"C Zhang","year":"2014","unstructured":"Zhang C, Lan T, Hou J, Li J, Fang R, Yang Z, Zhang M, Liu J, Liu B. NOX4 promotes non-small cell lung cancer cell proliferation and metastasis through positive feedback regulation of PI3K\/Akt signaling. Oncotarget. 2014;5:4392\u2013405.","journal-title":"Oncotarget"},{"key":"134_CR42","doi-asserted-by":"crossref","unstructured":"Xiang Z, Yuan W, Luo N, Wang Y, Tan K, Deng Y, Zhou X, Zhu C, Li Y, Liu M, et al. A novel human zinc finger protein ZNF540 interacts with MVP and inhibits transcriptional activities of the ERK signal pathway. Biochem Biophys Res Commun 2006, 347:288-296. https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/16815308 .","DOI":"10.1016\/j.bbrc.2006.06.076"},{"key":"134_CR43","doi-asserted-by":"publisher","first-page":"4476","DOI":"10.1182\/blood-2011-09-379958","volume":"119","author":"M Porcu","year":"2012","unstructured":"Porcu M, Kleppe M, Gianfelici V, Geerdens E, De Keersmaecker K, Tartaglia M, Foa R, Soulier J, Cauwelier B, Uyttebroeck A, et al. Mutation of the receptor tyrosine phosphatase PTPRC (CD45) in T-cell acute lymphoblastic leukemia. Blood. 2012;119:4476\u20139.","journal-title":"Blood"},{"key":"134_CR44","doi-asserted-by":"publisher","first-page":"e40568","DOI":"10.1371\/journal.pone.0040568","volume":"7","author":"T Sivik","year":"2012","unstructured":"Sivik T, Gunnarsson C, Fornander T, Nordenskjold B, Skoog L, Stal O, Jansson A. 17beta-Hydroxysteroid dehydrogenase type 14 is a predictive marker for tamoxifen response in oestrogen receptor positive breast cancer. PLoS One. 2012;7:e40568.","journal-title":"PLoS One"},{"key":"134_CR45","doi-asserted-by":"crossref","unstructured":"Grandoni F, Krysta P, Leonardi S, Ventre C. Utilitarian mechanism design for multi-objective optimization. In Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics. International Conference on Affective Computing and Intelligent Interaction and Workshops: [proceedings]. ACII (Conference); 2010. pp. 573\u2013584.","DOI":"10.1137\/1.9781611973075.48"},{"key":"134_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1755-8794-6-30","volume":"6","author":"H Wang","year":"2013","unstructured":"Wang H, Zhang H, Dai Z, Chen M-s, Yuan Z. TSG: a new algorithm for binary and multi-class cancer classification and informative genes selection. BMC Med Genomics. 2013;6:1\u201314.","journal-title":"BMC Med Genomics"},{"key":"134_CR47","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","volume":"45","author":"M Sokolova","year":"2009","unstructured":"Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Inf Process Manage. 2009;45:427\u201337.","journal-title":"Inf Process Manage"}],"container-title":["BioData Mining"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-017-0134-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13040-017-0134-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-017-0134-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:54:18Z","timestamp":1750204458000},"score":1,"resource":{"primary":{"URL":"https:\/\/biodatamining.biomedcentral.com\/articles\/10.1186\/s13040-017-0134-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,24]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,12]]}},"alternative-id":["134"],"URL":"https:\/\/doi.org\/10.1186\/s13040-017-0134-8","relation":{},"ISSN":["1756-0381"],"issn-type":[{"value":"1756-0381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,4,24]]},"assertion":[{"value":"14 December 2016","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2017","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2017","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"13"}}