{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T21:54:03Z","timestamp":1775598843577,"version":"3.50.1"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2012,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>A feature selection method in microarray gene expression data should be independent of platform, disease and dataset size. Our hypothesis is that among the statistically significant ranked genes in a gene list, there should be clusters of genes that share similar biological functions related to the investigated disease. Thus, instead of keeping <jats:italic>N<\/jats:italic> top ranked genes, it would be more appropriate to define and keep a number of gene cluster exemplars.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We propose a hybrid FS method (m<jats:italic>AP<\/jats:italic>-KL), which combines multiple hypothesis testing and affinity propagation (AP)-clustering algorithm along with the Krzanowski &amp; Lai cluster quality index, to select a small yet informative subset of genes. We applied m<jats:italic>AP<\/jats:italic>-KL on real microarray data, as well as on simulated data, and compared its performance against 13 other feature selection approaches. Across a variety of diseases and number of samples, m<jats:italic>AP<\/jats:italic>-KL presents competitive classification results, particularly in neuromuscular diseases, where its overall AUC score was 0.91. Furthermore, m<jats:italic>AP<\/jats:italic>-KL generates concise yet biologically relevant and informative <jats:italic>N<\/jats:italic>-gene expression signatures, which can serve as a valuable tool for diagnostic and prognostic purposes, as well as a source of potential disease biomarkers in a broad range of diseases.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>m<jats:italic>AP<\/jats:italic>-KL is a data-driven and classifier-independent hybrid feature selection method, which applies to any disease classification problem based on microarray data, regardless of the available samples. Combining multiple hypothesis testing and AP leads to subsets of genes, which classify unknown samples from both, small and large patient cohorts with high accuracy.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-13-270","type":"journal-article","created":{"date-parts":[[2012,10,17]],"date-time":"2012-10-17T12:14:43Z","timestamp":1350476083000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Combining multiple hypothesis testing and affinity propagation clustering leads to accurate, robust and sample size independent classification on gene expression data"],"prefix":"10.1186","volume":"13","author":[{"given":"Argiris","family":"Sakellariou","sequence":"first","affiliation":[]},{"given":"Despina","family":"Sanoudou","sequence":"additional","affiliation":[]},{"given":"George","family":"Spyrou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2012,10,17]]},"reference":[{"issue":"1","key":"5567_CR1","doi-asserted-by":"publisher","first-page":"20+","DOI":"10.1186\/1471-2105-10-20","volume":"10","author":"R Hu","year":"2009","unstructured":"Hu R, Qiu X, Glazko G, Klebanov L, Yakovlev A: Detecting intergene correlation changes in microarray analysis: a new approach to gene selection. BMC Bioinforma 2009, 10(1):20+. 10.1186\/1471-2105-10-20","journal-title":"BMC Bioinforma"},{"issue":"19","key":"5567_CR2","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","volume":"23","author":"Y Saeys","year":"2007","unstructured":"Saeys Y, Inza I, Larranaga P: A review of feature selection techniques in bioinformatics. Bioinformatics 2007, 23(19):2507\u20132517. 10.1093\/bioinformatics\/btm344","journal-title":"Bioinformatics"},{"key":"5567_CR3","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A: An introduction to variable and feature selection. J Mach Learn Res 2003, 3: 1157\u20131182.","journal-title":"J Mach Learn Res"},{"issue":"2","key":"5567_CR4","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.artmed.2004.01.007","volume":"31","author":"I Inza","year":"2004","unstructured":"Inza I, Larra\u00f1aga P, Blanco R, Cerrolaza AJ: Filter versus wrapper gene selection approaches in DNA microarray domains. Artificial intelligence in medicine 2004, 31(2):91\u2013103. 10.1016\/j.artmed.2004.01.007","journal-title":"Artificial intelligence in medicine"},{"issue":"4","key":"5567_CR5","doi-asserted-by":"publisher","first-page":"227","DOI":"10.2165\/00822942-200504040-00003","volume":"4","author":"M Hauskrecht","year":"2005","unstructured":"Hauskrecht M, Pelikan R, Malehorn DE, Bigbee WL, Lotze MT, Zeh HJ, Whitcomb DC, Lyons-Weiler J: Feature selection for classification of SELDI-TOF-MS proteomic profiles. Appl Bioinformatics 2005, 4(4):227\u2013246. 10.2165\/00822942-200504040-00003","journal-title":"Appl Bioinformatics"},{"issue":"5439","key":"5567_CR6","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"TR Golub","year":"1999","unstructured":"Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286(5439):531\u2013537. 10.1126\/science.286.5439.531","journal-title":"Science"},{"key":"5567_CR7","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1152\/physiolgenomics.2001.5.2.99","volume":"5","author":"ML Chow","year":"2001","unstructured":"Chow ML, Moler EJ, Mian IS: Identifying marker genes in transcription profiling data using a mixture of feature relevance experts. Physiol. Genomics 2001, 5: 99\u201311.","journal-title":"Physiol. Genomics"},{"key":"5567_CR8","first-page":"93","volume-title":"Statistical Analysis of Gene Expression Microarray Data","author":"S Dudoit","year":"2003","unstructured":"Dudoit S, Fridlyand J: Classification in microarray experiments. In Statistical Analysis of Gene Expression Microarray Data. Edited by: Speed TP. London: Chapman & Hall\/CRC; 2003:93\u2013158."},{"key":"5567_CR9","volume-title":"Correlation-based feature selection for machine learning","author":"M Hall","year":"1998","unstructured":"Hall M PhD thesis. In Correlation-based feature selection for machine learning. Hamilton NZ Waikato University: Department of Computer Science; 1998."},{"issue":"1","key":"5567_CR10","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1093\/bioinformatics\/19.1.37","volume":"19","author":"CH Ooi","year":"2003","unstructured":"Ooi CH, Tan P: Genetic Algorithms Applied to Multi-Class Prediction for the Analysis of Gene Expression Data. Bioinformatics 2003, 19(1):37\u201344. 10.1093\/bioinformatics\/19.1.37","journal-title":"Bioinformatics"},{"issue":"1","key":"5567_CR11","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1093\/bioinformatics\/19.1.45","volume":"19","author":"JM Deutsch","year":"2003","unstructured":"Deutsch JM: Evolutionary algorithms for finding optimal gene sets in microarray prediction. Bioinformatics 2003, 19(1):45\u201352. 10.1093\/bioinformatics\/19.1.45","journal-title":"Bioinformatics"},{"issue":"9","key":"5567_CR12","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.1093\/bioinformatics\/btl074","volume":"22","author":"V Trevino","year":"2006","unstructured":"Trevino V, Falciani F: GALGO: an r package for multivariate variable selection using genetic algorithms. Bioinformatics 2006, 22(9):1154\u20131156. 10.1093\/bioinformatics\/btl074","journal-title":"Bioinformatics"},{"key":"5567_CR13","first-page":"87","volume":"2","author":"J Wang","year":"2007","unstructured":"Wang J, Do KAA, Wen S, Tsavachidis S, McDonnell TJ, Logothetis CJ, Coombes KR: Merging microarray data, robust feature selection, and predicting prognosis in prostate cancer. Cancer informatics 2007, 2: 87\u201397.","journal-title":"Cancer informatics"},{"issue":"1","key":"5567_CR14","doi-asserted-by":"crossref","first-page":"5-32-32","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L: Random forests. Mach Learn 2001, 45(1):5\u201332\u201332.","journal-title":"Mach Learn"},{"key":"5567_CR15","volume-title":"Briefings in bioinformatics","author":"S Ma","year":"2011","unstructured":"Ma S, Dai Y: Principal component analysis based methods in bioinformatics studies. Briefings in bioinformatics 2011. 12(5). 12(5)."},{"key":"5567_CR16","first-page":"53","volume":"8","author":"J Jaeger","year":"2003","unstructured":"Jaeger J, Sengupta R, Ruzzo W: Improved gene selection for classification of microarrays. Pac Symp Biocomput 2003, 8: 53\u201364.","journal-title":"Pac Symp Biocomput"},{"key":"5567_CR17","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1198\/106186006X113430","volume":"15","author":"H Zou","year":"2004","unstructured":"Zou H, Hastie T, Tibshirani R: Sparse principal component analysis. J Comput Graph Stat 2004, 15: 265\u2013286.","journal-title":"J Comput Graph Stat"},{"key":"5567_CR18","first-page":"8","volume":"1","author":"RK Agrawal","year":"2007","unstructured":"Agrawal RK, Rajni Bala : A Hybrid Approach for Selection of Relevant Features for Microarray Datasets. International Journal of Computer and Information Engineering 2007, 1: 8.","journal-title":"International Journal of Computer and Information Engineering"},{"key":"5567_CR19","first-page":"Vol I","volume-title":"Proceedings of the International MultiConference of Engineers and Computer Scientists","author":"C Li-Yeh","year":"2008","unstructured":"Li-Yeh C, Chao-Hsuan K, Cheng-Hong Y: A Hybrid Both Filter and Wrapper Feature Selection Method for Microarray Classification. In Proceedings of the International MultiConference of Engineers and Computer Scientists. Hong Kong; 19\u201321 March 2008:Vol I."},{"key":"5567_CR20","first-page":"24","volume":"10","author":"Y Pengyi","year":"2009","unstructured":"Pengyi Y, Zili Z: An embedded two-layer feature selection approach for microarray data analysis. IEEE Intelligent Informatics Bulletin 2009, 10: 24\u201332.","journal-title":"IEEE Intelligent Informatics Bulletin"},{"issue":"Suppl 1","key":"5567_CR21","doi-asserted-by":"publisher","first-page":"S19","DOI":"10.1186\/1471-2105-10-S1-S19","volume":"10","author":"MR Hassan","year":"2009","unstructured":"Hassan MR, Hossain MM, Bailey J, Macintyre G, Ho JW, Ramamohanarao K: A voting approach to identify a small number of highly predictive genes using multiple classifiers. BMC Bioinforma 2009, 10(Suppl 1):S19. 10.1186\/1471-2105-10-S1-S19","journal-title":"BMC Bioinforma"},{"issue":"1","key":"5567_CR22","doi-asserted-by":"publisher","first-page":"359+","DOI":"10.1186\/1471-2105-7-359","volume":"7","author":"I Jeffery","year":"2006","unstructured":"Jeffery I, Higgins D, Culhane A: Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data. BMC Bioinforma 2006, 7(1):359+. 10.1186\/1471-2105-7-359","journal-title":"BMC Bioinforma"},{"key":"5567_CR23","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1038\/415530a","volume":"415","author":"LJ van \u2019t Veer","year":"2002","unstructured":"van \u2019t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AAM, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH: Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415: 530\u2013536. 10.1038\/415530a","journal-title":"Nature"},{"key":"5567_CR24","first-page":"251","volume-title":"Multiple Testing Procedures: R multtest Package and Applications to Genomics","author":"KS Pollard","year":"2005","unstructured":"Pollard KS, Dudoit S, van der Laan MJ: Multiple Testing Procedures: R multtest Package and Applications to Genomics. New York: Springer; 2005:251\u2013272."},{"issue":"5814","key":"5567_CR25","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1126\/science.1136800","volume":"315","author":"BJ Frey","year":"2007","unstructured":"Frey BJ, Dueck D: Clustering by passing messages between data points. Science 2007, 315(5814):972\u2013976. 10.1126\/science.1136800","journal-title":"Science"},{"key":"5567_CR26","doi-asserted-by":"publisher","first-page":"23","DOI":"10.2307\/2531893","volume":"44","author":"WJ Krzanowski","year":"1988","unstructured":"Krzanowski WJ, Lai YT: A criterion for determining the number of groups in a data set using sum of squares clustering. Biometrics 1988, 44: 23\u201334. 10.2307\/2531893","journal-title":"Biometrics"},{"key":"5567_CR27","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1145\/980972.980977","volume":"5","author":"B Hanczar","year":"2003","unstructured":"Hanczar B, Courtine M, Benis A, Hennegar C, Clement K, Zucker J-D: Improving classification of microarray data using prototype-based feature selection. SIGKDD Explor. Newslett 2003, 5: 23\u201330. 10.1145\/980972.980977","journal-title":"SIGKDD Explor. Newslett"},{"issue":"8","key":"5567_CR28","doi-asserted-by":"publisher","first-page":"1530","DOI":"10.1093\/bioinformatics\/bti192","volume":"21","author":"Y Wang","year":"2005","unstructured":"Wang Y, Makedon FS, Ford JC, Pearlman J: HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data. Bioinformatics 2005, 21(8):1530\u20131537. 10.1093\/bioinformatics\/bti192","journal-title":"Bioinformatics"},{"issue":"2","key":"5567_CR29","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1142\/S0219720005001004","volume":"3","author":"C Ding","year":"2005","unstructured":"Ding C, Peng H: Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 2005, 3(2):185\u2013205. 10.1142\/S0219720005001004","journal-title":"J Bioinform Comput Biol"},{"issue":"3","key":"5567_CR30","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1109\/TITB.2011.2130531","volume":"15","author":"A Sakellariou","year":"2011","unstructured":"Sakellariou A, Sanoudou D, Spyrou G: Investigating the minimum required number of genes for the classification of neuromuscular disease microarray data. IEEE Trans Inf Technol Biomed 2011, 15(3):349\u201355.","journal-title":"IEEE Trans Inf Technol Biomed"},{"key":"5567_CR31","first-page":"303","volume":"216","author":"M Walesiak","year":"2008","unstructured":"Walesiak M: Cluster analysis with ClusterSim computer program and R environment. Acta Universitatis Lodziniensis Folia Oeconomica 2008, 216: 303\u2013311.","journal-title":"Acta Universitatis Lodziniensis Folia Oeconomica"},{"key":"5567_CR32","volume-title":"R: A language and environment for statistical computing","author":"R Development Core Team","year":"2010","unstructured":"R Development Core Team R Foundation for Statistical Computing. In R: A language and environment for statistical computing. Vienna, Austria; 2010."},{"key":"5567_CR33","doi-asserted-by":"publisher","first-page":"2463","DOI":"10.1093\/bioinformatics\/btr406","volume":"27","author":"U Bodenhofer","year":"2011","unstructured":"Bodenhofer U, Kothmeier A, Hochreiter S: APCluster: an R package for affinity propagation clustering. Bioinformatics 2011, 27: 2463\u20132464. 10.1093\/bioinformatics\/btr406","journal-title":"Bioinformatics"},{"issue":"27","key":"5567_CR34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang CC, Lin CJ: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2011, 2(27):1\u201327.","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"issue":"1","key":"5567_CR35","first-page":"37","volume":"6","author":"DW Aha","year":"1991","unstructured":"Aha DW, Kibler D, Albert MK: Instance-based learning algorithms. Mach Learn 1991, 6(1):37\u201366.","journal-title":"Mach Learn"},{"issue":"1","key":"5567_CR36","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten I: The weka data mining software: an update. SIGKDD 2009, 11(1):10\u201318. 10.1145\/1656274.1656278","journal-title":"SIGKDD"},{"issue":"3","key":"5567_CR37","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1109\/TKDE.2005.50","volume":"17","author":"J Huang","year":"2005","unstructured":"Huang J, Ling CX: Using AUC and Accuracy in Evaluating Learning Algorithms. IEEE Transactions on Knowledge and Data Engineering 2005, 17(3):299\u2013310.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"Suppl 2","key":"5567_CR38","doi-asserted-by":"crossref","first-page":"S21+","DOI":"10.1186\/1471-2164-9-S2-S21","volume":"9","author":"R Hewett","year":"2008","unstructured":"Hewett R, Kijsanayothin P: Tumor classification ranking from microarray data. BMC Genomics 2008, 9(Suppl 2):S21+.","journal-title":"BMC Genomics"},{"issue":"5","key":"5567_CR39","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1093\/bioinformatics\/16.5.412","volume":"16","author":"P Baldi","year":"2000","unstructured":"Baldi P, Brunak S, Chauvin Y, Andersen CAF, Nielsen H: Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 2000, 16(5):412\u2013424. 10.1093\/bioinformatics\/16.5.412","journal-title":"Bioinformatics"},{"issue":"Pt 4","key":"5567_CR40","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1093\/brain\/awl023","volume":"129","author":"M Bakay","year":"2006","unstructured":"Bakay M, Wang Z, Melcon G, Schiltz L, Xuan J, Zhao P, Sartorelli V, Seo J, Pegoraro E, Angelini C, Shneiderman B, Escolar D, Chen YW, Winokur ST, Pachman LM, Fan C, Mandler R, Nevo Y, Gordon E, Zhu Y, Dong Y, Wang Y, Hoffman EP: Nuclear envelope dystrophies show a transcriptional fingerprint suggesting disruption of Rb-MyoD pathways in muscle regeneration. Brain 2006, 129(Pt 4):996\u20131013.","journal-title":"Brain"},{"issue":"8","key":"5567_CR41","doi-asserted-by":"publisher","first-page":"4666","DOI":"10.1073\/pnas.0330960100","volume":"100","author":"D Sanoudou","year":"2003","unstructured":"Sanoudou D, Haslett JN, Kho AT, Guo S, Gazda HT, Greenberg SA, Lidov HGW, Kohane IS, Kunkel LM, Beggs AH: Expression profiling reveals altered satellite cell numbers and glycolytic enzyme transcription in nemaline myopathy muscle. PNAS 2003, 100(8):4666\u20134671. 10.1073\/pnas.0330960100","journal-title":"PNAS"},{"issue":"8","key":"5567_CR42","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/S1471-4914(01)02089-5","volume":"7","author":"D Sanoudou","year":"2001","unstructured":"Sanoudou D, Beggs AH: Clinical and genetic heterogeneity in nemaline myopathy - A disease of skeletal muscle thin filaments. Trends in Molecular Medicine 2001, 7(8):362\u2013368. 10.1016\/S1471-4914(01)02089-5","journal-title":"Trends in Molecular Medicine"},{"issue":"12","key":"5567_CR43","doi-asserted-by":"publisher","first-page":"6745","DOI":"10.1073\/pnas.96.12.6745","volume":"96","author":"U Alon","year":"1999","unstructured":"Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, Levine AJ: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. PNAS 1999, 96(12):6745\u20136750. 10.1073\/pnas.96.12.6745","journal-title":"PNAS"},{"issue":"2","key":"5567_CR44","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/S1535-6108(02)00030-2","volume":"1","author":"D Singh","year":"2002","unstructured":"Singh D, Febbo PG, Ross K, Jackson DG, Manola J, Ladd C, Tamayo P, Renshaw AA, D\u2019Amico AV, Richie JP, Lander ES, Loda M, Kantoff PW, Golub TR, Sellers WR: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 2002, 1(2):203\u2013209. 10.1016\/S1535-6108(02)00030-2","journal-title":"Cancer Cell"},{"issue":"16","key":"5567_CR45","first-page":"5974","volume":"61","author":"JB Welsh","year":"2001","unstructured":"Welsh JB, Sapinoso LM, Su AI, Kern SG, Wang-Rodriguez J, Moskaluk CA, Frierson HF, Hampton GM: Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. Cancer Res 2001, 61(16):5974\u20135978.","journal-title":"Cancer Res"},{"key":"5567_CR46","doi-asserted-by":"crossref","first-page":"R16+","DOI":"10.1186\/gb-2005-6-2-r16","volume":"6","author":"SE Choe","year":"2005","unstructured":"Choe SE, Boutros M, Michelson AM, Church GM, Halfon MS: Preferred analysis methods for affymetrix GeneChips revealed by a wholly defined control dataset. Genome Biol 2005, 6: R16+.","journal-title":"Genome Biol"},{"key":"5567_CR47","doi-asserted-by":"crossref","first-page":"9","DOI":"10.2202\/1544-6115.1252","volume":"6","author":"R Opgen-Rhein","year":"2007","unstructured":"Opgen-Rhein R, Strimmer K: Accurate ranking of differentially expressed genes by a distribution-free shrinkage approach. Statist. Appl. Genet. Mol. Biol. 2007, 6: 9.","journal-title":"Statist. Appl. Genet. Mol. Biol"},{"issue":"20","key":"5567_CR48","doi-asserted-by":"publisher","first-page":"2700","DOI":"10.1093\/bioinformatics\/btp460","volume":"25","author":"V Zuber","year":"2009","unstructured":"Zuber V, Strimmer K: Gene ranking and biomarker discovery under correlation. Bioinformatics 2009, 25(20):2700\u20132707. 10.1093\/bioinformatics\/btp460","journal-title":"Bioinformatics"},{"key":"5567_CR49","doi-asserted-by":"publisher","first-page":"1600","DOI":"10.1093\/bioinformatics\/18.12.1600","volume":"18","author":"AC Culhane","year":"2002","unstructured":"Culhane AC, Perriere G, Considine EC, Cotter TG, Higgins DG: Between-group analysis of microarray data. Bioinformatics 2002, 18: 1600\u20131608. 10.1093\/bioinformatics\/18.12.1600","journal-title":"Bioinformatics"},{"issue":"3","key":"5567_CR50","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1111\/j.1467-9868.2007.005592.x","volume":"69","author":"DJ Storey","year":"2007","unstructured":"Storey DJ: The optimal discovery procedure: a new approach to simultaneous significance testing. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2007, 69(3):347\u2013368. 10.1111\/j.1467-9868.2007.005592.x","journal-title":"Journal of the Royal Statistical Society: Series B (Statistical Methodology)"},{"key":"5567_CR51","volume-title":"Resampling-based multiple testing: Examples and methods for p-value adjustment","author":"PH Westfall","year":"1993","unstructured":"Westfall PH, Young SS: Resampling-based multiple testing: Examples and methods for p-value adjustment. John Wiley & Sons; 1993."},{"issue":"1","key":"5567_CR52","first-page":"article14","volume":"3","author":"MJ van der Laan","year":"2004","unstructured":"van der Laan MJ, Dudoit S, Pollard KS: Multiple Testing. Part II. Step-Down Procedures for Control of the Family-Wise Error Rate. Statist. Appl. Genet. Mol. Biol. 2004, 3(1):article14.","journal-title":"Statist. Appl. Genet. Mol. Biol"},{"issue":"5","key":"5567_CR53","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1038\/ng0506-500","volume":"38","author":"M Reich","year":"2006","unstructured":"Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP: GenePattern 2.0. Nat Genet 2006, 38(5):500\u2013501. 10.1038\/ng0506-500","journal-title":"Nat Genet"},{"issue":"15","key":"5567_CR54","doi-asserted-by":"publisher","first-page":"1924","DOI":"10.1093\/bioinformatics\/btl196","volume":"22","author":"J Gould","year":"2006","unstructured":"Gould J, Getz G, Monti S, Reich M, Mesirov JP: Comparative gene marker selection suite. Bioinformatics 2006, 22(15):1924\u20131925. 10.1093\/bioinformatics\/btl196","journal-title":"Bioinformatics"},{"issue":"1","key":"5567_CR55","doi-asserted-by":"crossref","first-page":"article 33","DOI":"10.2202\/1544-6115.1075","volume":"3","author":"AL Boulesteix","year":"2004","unstructured":"Boulesteix AL: PLS dimension reduction for classification with microarray data. Statist. Appl. Genet. Mol. Biol 2004, 3(1):article 33.","journal-title":"Statist. Appl. Genet. Mol. Biol"},{"issue":"9","key":"5567_CR56","doi-asserted-by":"publisher","first-page":"5116","DOI":"10.1073\/pnas.091062498","volume":"98","author":"VG Tusher","year":"2001","unstructured":"Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. PNAS 2001, 98(9):5116\u20135121. 10.1073\/pnas.091062498","journal-title":"PNAS"},{"issue":"1","key":"5567_CR57","doi-asserted-by":"crossref","first-page":"article 3","DOI":"10.2202\/1544-6115.1027","volume":"3","author":"GK Smyth","year":"2004","unstructured":"Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statist. Appl. Genet. Mol. Biol 2004, 3(1):article 3.","journal-title":"Statist. Appl. Genet. Mol. Biol"},{"key":"5567_CR58","first-page":"397","volume-title":"Limma: linear models for microarray data","author":"GK Smyth","year":"2005","unstructured":"Smyth GK: Limma: linear models for microarray data. New York: Springer; 2005:397\u2013420."},{"issue":"19","key":"5567_CR59","doi-asserted-by":"publisher","first-page":"2430","DOI":"10.1093\/bioinformatics\/btl407","volume":"22","author":"C Sima","year":"2006","unstructured":"Sima C, Dougherty ER: What should be expected from feature selection in small-sample settings. Bioinformatics 2006, 22(19):2430\u20132436. 10.1093\/bioinformatics\/btl407","journal-title":"Bioinformatics"},{"key":"5567_CR60","first-page":"33","volume-title":"Proc. AusDM","author":"H Hu","year":"2006","unstructured":"Hu H, Li J, Plank AW, Wang H, Daggard G: A Comparative Study of Classification Methods For Microarray Data Analysis. Proc. AusDM 2006, 33\u201337."},{"issue":"21","key":"5567_CR61","doi-asserted-by":"publisher","first-page":"2635","DOI":"10.1093\/bioinformatics\/btl442","volume":"22","author":"R Shen","year":"2006","unstructured":"Shen R, Ghosh D, Chinnaiyan A, Meng Z: Eigengene-based linear discriminant model for tumor classification using gene expression microarray data. Bioinformatics 2006, 22(21):2635\u20132642. 10.1093\/bioinformatics\/btl442","journal-title":"Bioinformatics"},{"key":"5567_CR62","doi-asserted-by":"crossref","first-page":"R121+","DOI":"10.1186\/gb-2006-7-12-r121","volume":"7","author":"H Moon","year":"2006","unstructured":"Moon H, Ahn H, Kodell RL, Lin C-J, Baek S, Chen JJ: Classification methods for the development of genomic signatures from high-dimensional data. Genome Biol 2006, 7: R121+.","journal-title":"Genome Biol"},{"issue":"Suppl 3","key":"5567_CR63","first-page":"S75","volume":"2","author":"ACC Tan","year":"2003","unstructured":"Tan ACC, Gilbert D: Ensemble machine learning on gene expression data for cancer classification. Appl Bioinforma 2003, 2(Suppl 3):S75-S83.","journal-title":"Appl Bioinforma"},{"issue":"12","key":"5567_CR64","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1093\/bioinformatics\/17.12.1131","volume":"17","author":"L Li","year":"2001","unstructured":"Li L, Weinberg CR, Darden TA, Pedersen LG: Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA\/KNN method. Bioinformatics 2001, 17(12):1131\u20131142. 10.1093\/bioinformatics\/17.12.1131","journal-title":"Bioinformatics"},{"issue":"1","key":"5567_CR65","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1093\/bioinformatics\/18.1.39","volume":"18","author":"DV Nguyen","year":"2002","unstructured":"Nguyen DV, Rocke DM: Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 2002, 18(1):39\u201350. 10.1093\/bioinformatics\/18.1.39","journal-title":"Bioinformatics"},{"issue":"10","key":"5567_CR66","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1093\/bioinformatics\/16.10.906","volume":"16","author":"TS Furey","year":"2000","unstructured":"Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000, 16(10):906\u2013914. 10.1093\/bioinformatics\/16.10.906","journal-title":"Bioinformatics"},{"issue":"1","key":"5567_CR67","doi-asserted-by":"publisher","first-page":"136+","DOI":"10.1186\/1471-2105-5-136","volume":"5","author":"B Liu","year":"2004","unstructured":"Liu B, Cui Q, Jiang T, Ma S: A combinational feature selection and ensemble neural network method for classification of gene expression data. BMC Bioinforma 2004, 5(1):136+. 10.1186\/1471-2105-5-136","journal-title":"BMC Bioinforma"},{"issue":"3\u20134","key":"5567_CR68","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1089\/106652700750050943","volume":"7","author":"A Ben-Dor","year":"2000","unstructured":"Ben-Dor A, Bruhn L, Friedman N, Nachman I, Schummer M, Yakhini Z: Tissue classification with gene expression profiles. J Comput Biol 2000, 7(3\u20134):559\u2013583.","journal-title":"J Comput Biol"},{"issue":"5","key":"5567_CR69","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1093\/bioinformatics\/btg062","volume":"19","author":"A Antoniadis","year":"2003","unstructured":"Antoniadis A, Lambert-Lacroix S, Leblanc F: Effective dimension reduction methods for tumor classification using gene expression data. Bioinformatics 2003, 19(5):563\u2013570. 10.1093\/bioinformatics\/btg062","journal-title":"Bioinformatics"},{"key":"5567_CR70","volume-title":"Support vector machine classification of microarray data","author":"S Mukherjee","year":"1999","unstructured":"Mukherjee S, Tamayo P, Slonim D, Verri A, Golub T, Mesirov J, Poggio T AI Memo 1677. In Support vector machine classification of microarray data. Massachusetts Institute of Technology; 1999."},{"issue":"457","key":"5567_CR71","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1198\/016214502753479248","volume":"97","author":"S Dudoit","year":"2002","unstructured":"Dudoit S, Fridlyand J, Speed TP: Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 2002, 97(457):77\u201387. 10.1198\/016214502753479248","journal-title":"J Am Stat Assoc"},{"issue":"5","key":"5567_CR72","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1093\/bioinformatics\/btg462","volume":"20","author":"V Antonov","year":"2004","unstructured":"Antonov V, Tetko IV, Mader MT, Budczies J, Mewes HW: Optimization models for cancer classification: extracting gene interaction information from microarray expression data. Bioinformatics 2004, 20(5):644\u2013652. 10.1093\/bioinformatics\/btg462","journal-title":"Bioinformatics"},{"key":"5567_CR73","first-page":"104","volume-title":"Gene expression data classification with revised kernel partial least squares algorithm","author":"Z Liu","year":"2004","unstructured":"Liu Z, Chen D Proceedings of the 17th International FLAIRS Conference. In Gene expression data classification with revised kernel partial least squares algorithm. South Beach, Florida, USA; 2004:104\u2013108."},{"issue":"10","key":"5567_CR74","doi-asserted-by":"publisher","first-page":"6567","DOI":"10.1073\/pnas.082099299","volume":"99","author":"R Tibshirani","year":"2002","unstructured":"Tibshirani R, Hastie T, Narasimhan B, Chu G: Diagnosis of multiple cancer types by shrunken centroids of gene expression. PNAS 2002, 99(10):6567\u20136572. 10.1073\/pnas.082099299","journal-title":"PNAS"},{"key":"5567_CR75","doi-asserted-by":"publisher","first-page":"15000","DOI":"10.1073\/pnas.192571199","volume":"99","author":"JN Haslett","year":"2002","unstructured":"Haslett JN, Sanoudou D, Kho AT, Bennett RR, Greenberg SA, Kohane IS, Beggs AH, Kunkel LM: Gene expression comparison of biopsies from Duchenne muscular dystrophy (DMD) and normal skeletal muscle. PNAS 2002, 99: 15000\u201315005. 10.1073\/pnas.192571199","journal-title":"PNAS"},{"key":"5567_CR76","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/S0925-4773(00)00341-5","volume":"95","author":"PH Chu","year":"2000","unstructured":"Chu PH, Ruiz-Lozano P, Zhou Q, Cai C, Chen J: Expression Patterns of FHL\/SLIM Family Members Suggest Important Functional Roles in Skeletal Muscle and Cardiovascular System. Mech Dev 2000, 95: 259\u2013265. 10.1016\/S0925-4773(00)00341-5","journal-title":"Mech Dev"},{"issue":"6","key":"5567_CR77","doi-asserted-by":"publisher","first-page":"2401","DOI":"10.1182\/blood-2003-09-3160","volume":"103","author":"DC Yao","year":"2004","unstructured":"Yao DC, Tolan DR, Murray MF, Harris DJ, Darras BT, Geva A, Neufeld EJ: Hemolytic anemia and severe rhabdomyolysis caused by compound heterozygous mutations of the gene for erythrocyte\/muscle isozyme of aldolase, ALDOA(Arg303X\/Cys338Tyr). Blood 2004, 103(6):2401\u20133. 10.1182\/blood-2003-09-3160","journal-title":"Blood"},{"issue":"2","key":"5567_CR78","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1152\/physiolgenomics.00017.2007","volume":"32","author":"G de Aguilar JL","year":"2008","unstructured":"de Aguilar JL G, Niederhauser-Wiederkehr C, Halter B, de Tapia M, di Scala F, Demougin P, Dupuis L, Primig M, Meininger V, Loeffler JP: Gene profiling of skeletal muscle in an amyotrophic lateral sclerosis mouse model. Physiol Genomics 2008, 32(2):207\u201318.","journal-title":"Physiol Genomics"},{"key":"5567_CR79","unstructured":"MUSCULAR DYSTROPHY, LIMB-GIRDLE, TYPE 2B; LGMD2B http:\/\/omim.org\/entry\/253601"},{"issue":"5","key":"5567_CR80","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1002\/ana.20464","volume":"57","author":"SA Greenberg","year":"2005","unstructured":"Greenberg SA, Pinkus JL, Pinkus GS, Burleson T, Sanoudou D, Tawil R: Interferon-alpha\/beta-mediated innate immune mechanisms in dermatomyositis. Ann Neurol 2005, 57(5):664\u201378. 10.1002\/ana.20464","journal-title":"Ann Neurol"},{"key":"5567_CR81","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1038\/nature06915","volume":"452","author":"LJ van\u2019t Veer","year":"2008","unstructured":"van\u2019t Veer LJ, Bernards R: Enabling personalized cancer medicine through analysis of gene-expression patterns. Nature 2008, 452: 564\u201370. 10.1038\/nature06915","journal-title":"Nature"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-13-270.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T21:54:55Z","timestamp":1630533295000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/1471-2105-13-270"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,10,17]]},"references-count":81,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2012,12]]}},"alternative-id":["5567"],"URL":"https:\/\/doi.org\/10.1186\/1471-2105-13-270","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,10,17]]},"assertion":[{"value":"3 January 2012","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2012","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2012","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"270"}}