{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T09:57:31Z","timestamp":1778752651723,"version":"3.51.4"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: An accurate diagnostic and prediction will not be achieved unless the disease subtype status for every training sample used in the supervised learning step is accurately known. Such an assumption requires the existence of a perfect tool for disease diagnostic and classification, which is seldom available in the majority of the cases. Thus, the supervised learning step has to be conducted with a statistical model that contemplates and handles potential mislabeling in the input data.<\/jats:p><jats:p>Results: A procedure for handling potential mislabeling among training samples in the prediction of disease subtypes using gene expression data was proposed. A real data-based simulation study about the estrogen receptor status (ER+\/ER\u2212) of breast cancer patients was conducted. The results demonstrated that when 1\u20134 training samples (N = 30) were artificially mislabeled, the proposed method was able not only in correcting the ER status of mislabeled training samples but also more importantly in predicting the ER status of validation samples as well as using \u2018true\u2019 training data.<\/jats:p><jats:p>Availability: The programs (in Matlab) used for analysis are publicly available at<\/jats:p><jats:p>Contact: \u00a0rrekaya@uga.edu<\/jats:p>","DOI":"10.1093\/bioinformatics\/bti738","type":"journal-article","created":{"date-parts":[[2005,11,3]],"date-time":"2005-11-03T01:13:48Z","timestamp":1130980428000},"page":"317-325","source":"Crossref","is-referenced-by-count":27,"title":["A method for predicting disease subtypes in presence of misclassification among training samples using gene expression: application to human breast cancer"],"prefix":"10.1093","volume":"22","author":[{"given":"Wensheng","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Animal and Dairy Science, University of Georgia 1 \u00a0 1 \u00a0 \u00a0 Athens, GA 30602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Romdhane","family":"Rekaya","sequence":"additional","affiliation":[{"name":"Department of Animal and Dairy Science, University of Georgia 1 \u00a0 1 \u00a0 \u00a0 Athens, GA 30602, USA"},{"name":"Department of Statistics, University of Georgia 2 \u00a0 2 \u00a0 \u00a0 Athens, GA 30602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keith","family":"Bertrand","sequence":"additional","affiliation":[{"name":"Department of Animal and Dairy Science, University of Georgia 1 \u00a0 1 \u00a0 \u00a0 Athens, GA 30602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2005,11,2]]},"reference":[{"key":"2023012408331992600_b1","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1080\/01621459.1993.10476321","article-title":"Bayesian analysis of binary polychotomous response data","volume":"88","author":"Albert","year":"1993","journal-title":"J. Am. Stat. Assoc."},{"key":"2023012408331992600_b2","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1038\/35000501","article-title":"Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling","volume":"403","author":"Alizadeh","year":"2000","journal-title":"Nature"},{"key":"2023012408331992600_b3","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1093\/bioinformatics\/btg462","article-title":"Optimization models for cancer classification: extracting gene interaction information from microarray expression data","volume":"20","author":"Antonov","year":"2004","journal-title":"Bioinformatics"},{"key":"2023012408331992600_b4","doi-asserted-by":"crossref","DOI":"10.1186\/gb-2002-3-4-research0017","article-title":"New feature subset selection procedures for classification of expression profiles","volume":"3","author":"B\u00d8","year":"2002","journal-title":"Genome Biol."},{"key":"2023012408331992600_b5","volume-title":"Statistical Inference","author":"Casella","year":"2001"},{"key":"2023012408331992600_b6","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/978-1-4612-1276-8","volume-title":"Monte Carlo Methods in Bayesian Computation","author":"Chen","year":"2000"},{"key":"2023012408331992600_b7","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1198\/016214502753479248","article-title":"Comparison of discrimination methods for classification of tumors using gene expression data","volume":"97","author":"Dudoit","year":"2002","journal-title":"J. Am. Statist Assoc."},{"key":"2023012408331992600_b8","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1126\/science.286.5439.531","article-title":"Molecular classification of cancer: class discovery and class prediction by gene expression prediction","volume":"286","author":"Golub","year":"1999","journal-title":"Science"},{"key":"2023012408331992600_b9","doi-asserted-by":"crossref","DOI":"10.1007\/b98832","volume-title":"Ordinary Data Model","author":"Johnson","year":"1999"},{"key":"2023012408331992600_b10","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1038\/89044","article-title":"Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks","volume":"7","author":"Khan","year":"2001","journal-title":"Nat Med."},{"key":"2023012408331992600_b11","doi-asserted-by":"crossref","first-page":"research 0031.1","DOI":"10.1186\/gb-2001-2-8-research0031","article-title":"Cluster-Rasch models for microarray expression data","volume":"2","author":"Li","year":"2001","journal-title":"Genome Biol."},{"key":"2023012408331992600_b12","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1093\/biostatistics\/kxh011","article-title":"Bayesian analysis of binary prediction tree models for retrospectively sampled outcomes","volume":"5","author":"Pittman","year":"2004","journal-title":"Biostatistics"},{"key":"2023012408331992600_b13","doi-asserted-by":"crossref","first-page":"15149","DOI":"10.1073\/pnas.211566398","article-title":"Multiclass cancer diagnosis using tumor gene expression signatures","volume":"98","author":"Ramaswamy","year":"2001","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012408331992600_b14","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1111\/j.0006-341X.2001.01123.x","article-title":"Threshhold model for misclassified binary responses with applications to animal breeding","volume":"57","author":"Rekaya","year":"2001","journal-title":"Biometrics"},{"key":"2023012408331992600_b15","doi-asserted-by":"crossref","first-page":"2951","DOI":"10.1182\/blood-2003-01-0338","article-title":"Classification of pediatric acute lymphoblastic leukemia by gene expression profiling","volume":"102","author":"Ross","year":"2003","journal-title":"Blood"},{"key":"2023012408331992600_b16","first-page":"369","article-title":"Prediction and uncertainty in the analysis of gene expression profiles","volume":"2","author":"Spang","year":"2002","journal-title":"Silico Biol."},{"key":"2023012408331992600_b17","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1093\/bioinformatics\/bti033","article-title":"A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis","volume":"21","author":"Statnikov","year":"2005","journal-title":"Bioinformatics"},{"key":"2023012408331992600_b18","doi-asserted-by":"crossref","first-page":"6567","DOI":"10.1073\/pnas.082099299","article-title":"Diagnosis of multiple cancer types by shrunken centroids of gene expression","volume":"99","author":"Tibshirani","year":"2002","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012408331992600_b19","doi-asserted-by":"crossref","first-page":"5116","DOI":"10.1073\/pnas.091062498","article-title":"Significance analysis of microarrays applied to the ionizing radiation response","volume":"98","author":"Tusher","year":"2001","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012408331992600_b20","article-title":"DNA microarray data analysis and regression modeling for genetic expression profiling","author":"West","year":"2000"},{"key":"2023012408331992600_b21","doi-asserted-by":"crossref","first-page":"11462","DOI":"10.1073\/pnas.201162998","article-title":"Predicting the clinical status of human breast cancer by using gene expression profiles","volume":"98","author":"West","year":"2001","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023012408331992600_b22","first-page":"723","article-title":"Bayesian factor regression models in the \u2018Large p, Small n\u2019 paradigm","volume":"7","author":"West","year":"2003","journal-title":"Bayesian Statistics"},{"key":"2023012408331992600_b23","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/S1535-6108(02)00032-6","article-title":"Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling","volume":"1","author":"Yeoh","year":"2002","journal-title":"Cancer Cell"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/22\/3\/317\/48838729\/bioinformatics_22_3_317.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/22\/3\/317\/48838729\/bioinformatics_22_3_317.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T11:21:14Z","timestamp":1706700074000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/22\/3\/317\/219863"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2005,11,2]]},"references-count":23,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2006,2,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bti738","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2006,2,1]]},"published":{"date-parts":[[2005,11,2]]}}}