{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T17:26:11Z","timestamp":1786987571738,"version":"build-2736575974"},"reference-count":61,"publisher":"American Association for the Advancement of Science (AAAS)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:00:00Z","timestamp":1657497600000},"content-version":"vor","delay-in-days":191,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"publisher","award":["CSTC2019JCYJ-MSXMX0527"],"award-info":[{"award-number":["CSTC2019JCYJ-MSXMX0527"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007839","name":"Yunnan University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007839","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007957","name":"Chongqing Municipal Education Commission","doi-asserted-by":"publisher","award":["KJQN202100538"],"award-info":[{"award-number":["KJQN202100538"]}],"id":[{"id":"10.13039\/501100007957","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012397","name":"Army Medical University","doi-asserted-by":"publisher","award":["2019R054"],"award-info":[{"award-number":["2019R054"]}],"id":[{"id":"10.13039\/501100012397","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["csbj.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Comput Struct Biotechnol J"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1016\/j.csbj.2022.07.022","type":"journal-article","created":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T22:45:45Z","timestamp":1657752345000},"page":"3783-3795","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":5,"title":["MSPJ: Discovering potential biomarkers in small gene expression datasets\n                    <i>via<\/i>\n                    ensemble learning"],"prefix":"10.34133","volume":"20","author":[{"given":"HuaChun","family":"Yin","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Xinqiao Hospital, The Army Medical University, Chongqing 400037, China"},{"name":"College of Life Sciences, Chongqing Normal University, Chongqing 401331, China"},{"name":"Department of Neurobiology, Chongqing Key Laboratory of Neurobiology, The Army Medical University, Chongqing 400038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JingXin","family":"Tao","sequence":"additional","affiliation":[{"name":"College of Life Sciences, Chongqing Normal University, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyang","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Xinqiao Hospital, The Army Medical University, Chongqing 400037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Xiong","sequence":"additional","affiliation":[{"name":"Department of Neurobiology, Chongqing Key Laboratory of Neurobiology, The Army Medical University, Chongqing 400038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"College of Life Sciences, Chongqing Normal University, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Xinqiao Hospital, The Army Medical University, Chongqing 400037, China"},{"name":"Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Xinqiao Hospital, The Army Medical University, Chongqing 400037, China"},{"name":"Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2022,7,18]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrg.2016.10"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamaneurol.2017.0357"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1111\/febs.15051"},{"key":"e_1_3_5_5_2","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1038\/s41467-021-21697-0","article-title":"Genome editing reveals fitness effects of a gene for sexual dichromatism in Sulawesian fishes","volume":"12","author":"Ansai S.","year":"2021","unstructured":"Ansai S., Mochida K., Fujimoto S., Mokodongan D.F., Sumarto B.K.A., Masengi K.W.A. et al: Genome editing reveals fitness effects of a gene for sexual dichromatism in Sulawesian fishes. Nat Commun 2021; 12: 1350.","journal-title":"Nat Commun"},{"key":"e_1_3_5_6_2","doi-asserted-by":"crossref","first-page":"5731","DOI":"10.1038\/s41467-021-25950-4","article-title":"Elucidating gene expression adaptation of phylogenetically divergent coral holobionts under heat stress","volume":"12","author":"Avila-Magana V.","year":"2021","unstructured":"Avila-Magana V., Kamel B., DeSalvo M., Gomez-Campo K., Enriquez S., Kitano H. et al: Elucidating gene expression adaptation of phylogenetically divergent coral holobionts under heat stress. Nat Commun 2021; 12: 5731.","journal-title":"Nat Commun"},{"key":"e_1_3_5_7_2","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1038\/s41467-021-27937-7","article-title":"Signatures of selection in recently domesticated macadamia","volume":"13","author":"Lin J.","year":"2022","unstructured":"Lin J., Zhang W., Zhang X., Ma X., Zhang S., Chen S. et al: Signatures of selection in recently domesticated macadamia. Nat Commun 2022; 13: 242.","journal-title":"Nat Commun"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrn3475"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-07-1601"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms15180"},{"key":"e_1_3_5_11_2","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2019.12644","article-title":"Sample size estimates for cluster-randomized trials in hospital infection control and antimicrobial stewardship","volume":"2","author":"Blanco N.","year":"2019","unstructured":"Blanco N., Harris A.D., Magder L.S., Jernigan J.A., Reddy S.C., O'Hagan J. et al: Sample size estimates for cluster-randomized trials in hospital infection control and antimicrobial stewardship. JAMA Netw Open 2019; 2: e1912644.","journal-title":"JAMA Netw Open"},{"key":"e_1_3_5_12_2","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.46323","article-title":"A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task","volume":"8","author":"Verbruggen F.","year":"2019","unstructured":"Verbruggen F., Aron A.R., Band G.P., Beste C., Bissett P.G., Brockett A.T. et al: A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task. Elife 2019; 8.","journal-title":"Elife"},{"key":"e_1_3_5_13_2","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1093\/bioinformatics\/bti294","article-title":"Estimating misclassification error with small samples via bootstrap cross-validation","volume":"21","author":"Fu W.J.","year":"2005","unstructured":"Fu W.J., Carroll R.J. and Wang S.: Estimating misclassification error with small samples via bootstrap cross-validation. Bioinformatics 2005; 21: 1979.","journal-title":"Bioinformatics"},{"key":"e_1_3_5_14_2","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1186\/1471-2105-11-400","article-title":"Intensity dependent estimation of noise in microarrays improves detection of differentially expressed genes","volume":"11","author":"Zeisel A.","year":"2010","unstructured":"Zeisel A., Amir A., Kostler W.J. and Domany E.: Intensity dependent estimation of noise in microarrays improves detection of differentially expressed genes. BMC Bioinf 2010; 11: 400.","journal-title":"BMC Bioinf"},{"key":"e_1_3_5_15_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz627"},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl476"},{"key":"e_1_3_5_17_2","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1186\/1471-2105-15-116","article-title":"ShrinkBayes: a versatile R-package for analysis of count-based sequencing data in complex study designs","volume":"15","author":"van de Wiel M.A.","year":"2014","unstructured":"van de Wiel M.A., Neerincx M., Buffart T.E., Sie D. and Verheul H.M.: ShrinkBayes: a versatile R-package for analysis of count-based sequencing data in complex study designs. BMC Bioinf 2014; 15: 116.","journal-title":"BMC Bioinf"},{"key":"e_1_3_5_18_2","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1016\/S0140-6736(05)17866-0","article-title":"Prediction of cancer outcome with microarrays: a multiple random validation strategy","volume":"365","author":"Michiels S.","year":"2005","unstructured":"Michiels S., Koscielny S. and Hill C.: Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 2005; 365: 488.","journal-title":"Lancet"},{"key":"e_1_3_5_19_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkw797"},{"key":"e_1_3_5_20_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-genom-091212-153520"},{"key":"e_1_3_5_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.01.060"},{"key":"e_1_3_5_22_2","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1093\/bib\/bbz049","article-title":"Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data","volume":"21","author":"Yang Q.","year":"2020","unstructured":"Yang Q., Li B., Tang J., Cui X., Wang Y., Li X. et al: Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data. Brief Bioinform 2020; 21: 1058.","journal-title":"Brief Bioinform"},{"key":"e_1_3_5_23_2","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1038\/cr.2016.83","article-title":"Distribution of IgG galactosylation as a promising biomarker for cancer screening in multiple cancer types","volume":"26","author":"Ren S.","year":"2016","unstructured":"Ren S., Zhang Z., Xu C., Guo L., Lu R., Sun Y. et al: Distribution of IgG galactosylation as a promising biomarker for cancer screening in multiple cancer types. Cell Res 2016; 26: 963.","journal-title":"Cell Res"},{"key":"e_1_3_5_24_2","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.4096"},{"key":"e_1_3_5_25_2","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1001\/jamapsychiatry.2019.1530","article-title":"Evaluation of Differences in Individual Treatment Response in Schizophrenia Spectrum Disorders: A Meta-analysis","volume":"76","author":"Winkelbeiner S.","year":"2019","unstructured":"Winkelbeiner S., Leucht S., Kane J.M. and Homan P.: Evaluation of Differences in Individual Treatment Response in Schizophrenia Spectrum Disorders: A Meta-analysis. JAMA Psychiatry 2019; 76: 1063.","journal-title":"JAMA Psychiatry"},{"key":"e_1_3_5_26_2","doi-asserted-by":"crossref","first-page":"S12","DOI":"10.1186\/1471-2105-7-S2-S12","article-title":"Improving the performance of SVM-RFE to select genes in microarray data","volume":"7","author":"Ding Y.","year":"2006","unstructured":"Ding Y. and Wilkins D.: Improving the performance of SVM-RFE to select genes in microarray data. BMC Bioinf 2006; 7: S12.","journal-title":"BMC Bioinf"},{"key":"e_1_3_5_27_2","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gng052","article-title":"Testing for differentially expressed genes with microarray data","volume":"31","author":"Tsai C.A.","year":"2003","unstructured":"Tsai C.A., Chen Y.J. and Chen J.J.: Testing for differentially expressed genes with microarray data. Nucleic Acids Res 2003; 31: e52.","journal-title":"Nucleic Acids Res"},{"key":"e_1_3_5_28_2","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1093\/bioinformatics\/btl548","article-title":"Estimating p-values in small microarray experiments","volume":"23","author":"Yang H.","year":"2007","unstructured":"Yang H. and Churchill G.: Estimating p-values in small microarray experiments. Bioinformatics 2007; 23: 38.","journal-title":"Bioinformatics"},{"key":"e_1_3_5_29_2","article-title":"meta: An R Package for Meta-Analysis","author":"Schwarzer G.","year":"2007","unstructured":"Schwarzer G.: meta: An R Package for Meta-Analysis. R News 2007; .","journal-title":"R News"},{"key":"e_1_3_5_30_2","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1109\/TCBB.2007.1028","article-title":"Development of two-stage SVM-RFE gene selection strategy for microarray expression data analysis","volume":"4","author":"Tang Y.","year":"2007","unstructured":"Tang Y., Zhang Y.Q. and Huang Z.: Development of two-stage SVM-RFE gene selection strategy for microarray expression data analysis. IEEE\/ACM Trans Comput Biol Bioinform 2007; 4: 365.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"e_1_3_5_31_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3390\/microarrays2020115","article-title":"A flexible microarray data simulation model","volume":"2","author":"Dembele D.","year":"2013","unstructured":"Dembele D.: A flexible microarray data simulation model. Microarrays (Basel) 2013; 2: 115.","journal-title":"Microarrays (Basel)"},{"key":"e_1_3_5_32_2","doi-asserted-by":"crossref","first-page":"3276","DOI":"10.1093\/bioinformatics\/btaa105","article-title":"SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data","volume":"36","author":"Assefa A.T.","year":"2020","unstructured":"Assefa A.T., Vandesompele J. and Thas O.: SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data. Bioinformatics 2020; 36: 3276.","journal-title":"Bioinformatics"},{"key":"e_1_3_5_33_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btg405"},{"key":"e_1_3_5_34_2","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.3871"},{"key":"e_1_3_5_35_2","article-title":"preprocessCore: a collection of pre-processing functions","author":"Bolstad B.","year":"2013","unstructured":"Bolstad B.: preprocessCore: a collection of pre-processing functions. R Package Version 2013; .","journal-title":"R Package Version"},{"key":"e_1_3_5_36_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkv007"},{"key":"e_1_3_5_37_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.091062498"},{"key":"e_1_3_5_38_2","volume-title":"Multiple testing procedures: R multtest package and applications to genomicsBioinformatics and computational biology solutions using R and bioconductor","author":"Katherine S.","year":"2005","unstructured":"Katherine S., Pollard S.D. and van der Laan M.J.: Multiple testing procedures: R multtest package and applications to genomics. Bioinformatics and computational biology solutions using R and bioconductor. : 2005."},{"key":"e_1_3_5_39_2","doi-asserted-by":"crossref","first-page":"2774","DOI":"10.1093\/bioinformatics\/btx292","article-title":"RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets","volume":"33","author":"Del Carratore F.","year":"2017","unstructured":"Del Carratore F., Jankevics A., Eisinga R., Heskes T., Hong F. and Breitling R.: RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets. Bioinformatics 2017; 33: 2774.","journal-title":"Bioinformatics"},{"key":"e_1_3_5_40_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104387","article-title":"KnowSeq R-Bioc package: The automatic smart gene expression tool for retrieving relevant biological knowledge","volume":"133","author":"Castillo-Secilla D.","year":"2021","unstructured":"Castillo-Secilla D., Galvez J.M., Carrillo-Perez F., Verona-Almeida M., Redondo-Sanchez D., Ortuno F.M. et al: KnowSeq R-Bioc package: The automatic smart gene expression tool for retrieving relevant biological knowledge. Comput Biol Med 2021; 133: 104387.","journal-title":"Comput Biol Med"},{"key":"e_1_3_5_41_2","unstructured":"F Arag\u00f3n Roy\u00f3n AJV A Arauzo Azofra: FSinR: an exhaustive package for feature selection. 2020 arXiv:2002.10330v1."},{"key":"e_1_3_5_42_2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman L.","year":"2001","unstructured":"Breiman L.: Random forests. Machine Learn 2001; 45: 5.","journal-title":"Machine Learn"},{"key":"e_1_3_5_43_2","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab354","article-title":"Benchmark of filter methods for feature selection in high-dimensional gene expression survival data","volume":"23","author":"Bommert A.","year":"2022","unstructured":"Bommert A., Welchowski T., Schmid M. and Rahnenfuhrer J.: Benchmark of filter methods for feature selection in high-dimensional gene expression survival data. Brief Bioinform 2022; 23.","journal-title":"Brief Bioinform"},{"key":"e_1_3_5_44_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106270","article-title":"A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier","volume":"205","author":"Shaban W.M.","year":"2020","unstructured":"Shaban W.M., Rabie A.H., Saleh A.I. and Abo-Elsoud M.A.: A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier. Knowl Based Syst 2020; 205: 106270.","journal-title":"Knowl Based Syst"},{"key":"e_1_3_5_45_2","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.1109\/TNNLS.2018.2878400","article-title":"Biased random forest for dealing with the class imbalance problem","volume":"30","author":"Bader-El-Den M.","year":"2019","unstructured":"Bader-El-Den M., Teitei E. and Perry T.: Biased random forest for dealing with the class imbalance problem. IEEE Trans Neural Netw Learn Syst 2019; 30: 2163.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"e_1_3_5_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.xinn.2021.100141"},{"key":"e_1_3_5_47_2","article-title":"Package \u2018e1071\u2019","author":"David Meyer E.D.","year":"2019","unstructured":"David Meyer E.D., Hornik Kurt, Weingessel Andreas, Leisch Friedrich, Chang Chih-Chung and Lin Chih-Chen: Package \u2018e1071\u2019. R J 2019; .","journal-title":"R J"},{"key":"e_1_3_5_48_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti623"},{"key":"e_1_3_5_49_2","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1109\/TIP.2002.999673","article-title":"A complexity-bounded motion estimation algorithm","volume":"11","author":"Chimienti A.","year":"2002","unstructured":"Chimienti A., Ferraris C. and Pau D.: A complexity-bounded motion estimation algorithm. IEEE Trans Image Process 2002; 11: 387.","journal-title":"IEEE Trans Image Process"},{"key":"e_1_3_5_50_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-019-0071-9"},{"key":"e_1_3_5_51_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccr.2009.07.016"},{"key":"e_1_3_5_52_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature09715"},{"key":"e_1_3_5_53_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0001651"},{"key":"e_1_3_5_54_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-019-2599-6"},{"key":"e_1_3_5_55_2","article-title":"scDEA: differential expression analysis in single-cell RNA-sequencing data via ensemble learning","volume":"23","author":"Li H.S.","year":"2022","unstructured":"Li H.S., Ou-Yang L., Zhu Y., Yan H. and Zhang X.F.: scDEA: differential expression analysis in single-cell RNA-sequencing data via ensemble learning. Brief Bioinform 2022; 23.","journal-title":"Brief Bioinform"},{"key":"e_1_3_5_56_2","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1186\/1756-0500-3-277","article-title":"EMA - A R package for Easy Microarray data analysis","volume":"3","author":"Servant N.","year":"2010","unstructured":"Servant N., Gravier E., Gestraud P., Laurent C., Paccard C., Biton A. et al: EMA - A R package for Easy Microarray data analysis. BMC Res Notes 2010; 3: 277.","journal-title":"BMC Res Notes"},{"key":"e_1_3_5_57_2","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1186\/s12859-018-2264-5","article-title":"Random forest versus logistic regression: a large-scale benchmark experiment","volume":"19","author":"Couronne R.","year":"2018","unstructured":"Couronne R., Probst P. and Boulesteix A.L.: Random forest versus logistic regression: a large-scale benchmark experiment. BMC Bioinf 2018; 19: 270.","journal-title":"BMC Bioinf"},{"key":"e_1_3_5_58_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygeno.2019.11.004"},{"key":"e_1_3_5_59_2","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1109\/TCBB.2009.36","article-title":"Nonnegative principal component analysis for cancer molecular pattern discovery","volume":"7","author":"Xiaoxu H.","year":"2009","unstructured":"Xiaoxu H.: Nonnegative principal component analysis for cancer molecular pattern discovery. IEEE\/ACM Trans Comput Biol Bioinform 2009; 7: 537.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"e_1_3_5_60_2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1186\/s13059-020-02053-9","article-title":"Analysis of transcript-deleterious variants in Mendelian disorders: implications for RNA-based diagnostics","volume":"21","author":"Maddirevula S.","year":"2020","unstructured":"Maddirevula S., Kuwahara H., Ewida N., Shamseldin H.E., Patel N., Alzahrani F. et al: Analysis of transcript-deleterious variants in Mendelian disorders: implications for RNA-based diagnostics. Genome Biol 2020; 21: 145.","journal-title":"Genome Biol"},{"key":"e_1_3_5_61_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.artmed.2014.12.004","article-title":"Improving the Mann-Whitney statistical test for feature selection: an approach in breast cancer diagnosis on mammography","volume":"63","author":"Perez N.P.","year":"2015","unstructured":"Perez N.P., Guevara Lopez M.A., Silva A. and Ramos I.: Improving the Mann-Whitney statistical test for feature selection: an approach in breast cancer diagnosis on mammography. Artif Intell Med 2015; 63: 19.","journal-title":"Artif Intell Med"},{"key":"e_1_3_5_62_2","doi-asserted-by":"crossref","DOI":"10.1016\/S1470-2045(21)00656-2","article-title":"SABR for operable stage I non-small-cell lung cancer: comparison to surgery","volume":"22","author":"Zhang J.","year":"2021","unstructured":"Zhang J., Liu L., Zhao K., Guo C. and Li S.: SABR for operable stage I non-small-cell lung cancer: comparison to surgery. Lancet Oncol 2021; 22: e536.","journal-title":"Lancet Oncol"}],"container-title":["Computational and Structural Biotechnology Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2001037022003026?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2001037022003026?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/spj.science.org\/doi\/pdf\/10.1016\/j.csbj.2022.07.022","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T15:48:02Z","timestamp":1779032882000},"score":1,"resource":{"primary":{"URL":"https:\/\/spj.science.org\/doi\/10.1016\/j.csbj.2022.07.022"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":61,"alternative-id":["10.1016\/j.csbj.2022.07.022"],"URL":"https:\/\/doi.org\/10.1016\/j.csbj.2022.07.022","relation":{},"ISSN":["2001-0370"],"issn-type":[{"value":"2001-0370","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MSPJ: Discovering potential biomarkers in small gene expression datasets via ensemble learning","name":"articletitle","label":"Article Title"},{"value":"Computational and Structural Biotechnology Journal","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.csbj.2022.07.022","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.","name":"copyright","label":"Copyright"}]}}