{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T19:45:16Z","timestamp":1769024716446,"version":"3.49.0"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"19","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Two important questions for the analysis of gene expression measurements from different sample classes are (1) how to classify samples and (2) how to identify meaningful gene signatures (ranked gene lists) exhibiting the differences between classes and sample subsets. Solutions to both questions have immediate biological and biomedical applications. To achieve optimal classification performance, a suitable combination of classifier and gene selection method needs to be specifically selected for a given dataset. The selected gene signatures can be unstable and the resulting classification accuracy unreliable, particularly when considering different subsets of samples. Both unstable gene signatures and overestimated classification accuracy can impair biological conclusions.<\/jats:p>\n               <jats:p>Methods: We address these two issues by repeatedly evaluating the classification performance of all models, i.e. pairwise combinations of various gene selection and classification methods, for random subsets of arrays (sampling). A model score is used to select the most appropriate model for the given dataset. Consensus gene signatures are constructed by extracting those genes frequently selected over many samplings. Sampling additionally permits measurement of the stability of the classification performance for each model, which serves as a measure of model reliability.<\/jats:p>\n               <jats:p>Results: We analyzed a large gene expression dataset with 78 measurements of four different cartilage sample classes. Classifiers trained on subsets of measurements frequently produce models with highly variable performance. Our approach provides reliable classification performance estimates via sampling. In addition to reliable classification performance, we determined stable consensus signatures (i.e. gene lists) for sample classes. Manual literature screening showed that these genes are highly relevant to our gene expression experiment with osteoarthritic cartilage. We compared our approach to others based on a publicly available dataset on breast cancer.<\/jats:p>\n               <jats:p>Availability: R package at<\/jats:p>\n               <jats:p>Contact: \u00a0ralf.zimmer@bio.ifi.lmu.de<\/jats:p>","DOI":"10.1093\/bioinformatics\/btl400","type":"journal-article","created":{"date-parts":[[2006,8,2]],"date-time":"2006-08-02T18:47:44Z","timestamp":1154544464000},"page":"2356-2363","source":"Crossref","is-referenced-by-count":76,"title":["Reliable gene signatures for microarray classification: assessment of stability and performance"],"prefix":"10.1093","volume":"22","author":[{"given":"Chad A.","family":"Davis","sequence":"first","affiliation":[{"name":"Institute of Informatics, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, Amalienstrasse 17 \u00a0 80333 Munich, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabian","family":"Gerick","sequence":"additional","affiliation":[{"name":"Institute of Informatics, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, Amalienstrasse 17 \u00a0 80333 Munich, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Volker","family":"Hintermair","sequence":"additional","affiliation":[{"name":"Institute of Informatics, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, Amalienstrasse 17 \u00a0 80333 Munich, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caroline C.","family":"Friedel","sequence":"additional","affiliation":[{"name":"Institute of Informatics, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, Amalienstrasse 17 \u00a0 80333 Munich, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katrin","family":"Fundel","sequence":"additional","affiliation":[{"name":"Institute of Informatics, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, Amalienstrasse 17 \u00a0 80333 Munich, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"K\u00fcffner","sequence":"additional","affiliation":[{"name":"Institute of Informatics, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, Amalienstrasse 17 \u00a0 80333 Munich, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ralf","family":"Zimmer","sequence":"additional","affiliation":[{"name":"Institute of Informatics, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, Amalienstrasse 17 \u00a0 80333 Munich, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2006,7,31]]},"reference":[{"key":"2023012409234064900_b1","doi-asserted-by":"crossref","DOI":"10.1002\/art.22174","article-title":"Large-scale gene expression profiling major pathogenetic pathways of cartilage degeneration in osteoarthritis","volume-title":"Arthritis and Rheum","author":"Aigner","year":"2006"},{"key":"2023012409234064900_b2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: tool for the unification of biology. The Gene Ontology Consortium","volume":"25","author":"Ashburner","year":"2000","journal-title":"Nature Genet."},{"key":"2023012409234064900_b3","first-page":"1229","article-title":"Dimensionality reduction via sparse support vector machines","volume-title":"J. Mach. Learn. Res.","author":"Bi","year":"2003"},{"key":"2023012409234064900_b4","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1145\/130385.130401","article-title":"A training algorithm for optimal margin classifiers","volume-title":"COLT '92: Proceedings of the Fifth Annual Workshop on Computational Learning Theory","author":"Boser","year":"1992"},{"key":"2023012409234064900_b5","volume-title":"Classification and Regression Trees","author":"Breiman","year":"1984"},{"key":"2023012409234064900_b6","first-page":"379","article-title":"LAM: An open cluster environment for MPI","volume-title":"Proceedings of Supercomputing Symposium 94","author":"Burns","year":"1994"},{"key":"2023012409234064900_b7","author":"Chang","year":"2001"},{"key":"2023012409234064900_b8","first-page":"98","article-title":"Global functional profiling of gene expression","volume":"81","author":"Draghici","year":"2003","journal-title":"Genomics"},{"key":"2023012409234064900_b9","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/0-387-21679-0_3","article-title":"Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data","volume-title":"The Analysis of Gene Expression Data: Methods and Software","author":"Dudoit","year":"2003"},{"key":"2023012409234064900_b10","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1198\/016214502753479248","article-title":"Comparison of discrimination methods for the classification of tumors using gene expression data","volume":"97","author":"Dudoit","year":"2002","journal-title":"J. Am. Stat. Assoc."},{"key":"2023012409234064900_b11","first-page":"77","article-title":"Data Processing Effects on the Interpretation of Microarray Gene Expresssion Experiments","volume-title":"German Conference on Bioinformatics (GCB) 2005, Hamburg, Lecture Notes in Informatics","author":"Fundel","year":"2005"},{"key":"2023012409234064900_b12","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learning Res."},{"key":"2023012409234064900_b13","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-21606-5","volume-title":"The Elements of Statistical Learning","author":"Hastie","year":"2001"},{"key":"2023012409234064900_b14","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.artmed.2004.01.007","article-title":"Filter versus wrapper gene selection approaches in DNA microarray domains","volume":"31","author":"Inza","year":"2004","journal-title":"Artif. Intell. Med."},{"key":"2023012409234064900_b15","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.1093\/bioinformatics\/bti292","article-title":"Molecular decomposition of complex clinical phenotypes using biologically structured analysis of microarray data","volume":"21","author":"Lottaz","year":"2005","journal-title":"Bioinformatics"},{"key":"2023012409234064900_b16","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","year":"2005","journal-title":"Lancet"},{"key":"2023012409234064900_b17","volume-title":"Machine Learning","author":"Mitchell","year":"1997"},{"key":"2023012409234064900_b18","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":"2023012409234064900_b19","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":"2023012409234064900_b20","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1038\/415530a","article-title":"Gene expression profiling predicts clinical outcome of breast cancer","volume":"415","author":"van't Veer","year":"2002","journal-title":"Nature"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/22\/19\/2356\/48841249\/bioinformatics_22_19_2356.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/22\/19\/2356\/48841249\/bioinformatics_22_19_2356.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T10:09:29Z","timestamp":1674554969000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/22\/19\/2356\/241157"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006,7,31]]},"references-count":20,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2006,10,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btl400","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2006,10,1]]},"published":{"date-parts":[[2006,7,31]]}}}