{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T04:17:29Z","timestamp":1778645849721,"version":"3.51.4"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2016,12,6]],"date-time":"2016-12-06T00:00:00Z","timestamp":1480982400000},"content-version":"vor","delay-in-days":15,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["GM102365, LM05652, GM61374"],"award-info":[{"award-number":["GM102365, LM05652, GM61374"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["IC2014-1387"],"award-info":[{"award-number":["IC2014-1387"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Microarray measurements of gene expression constitute a large fraction of publicly shared biological data, and are available in the Gene Expression Omnibus (GEO). Many studies use GEO data to shape hypotheses and improve statistical power. Within GEO, the Affymetrix HG-U133A and HG-U133 Plus 2.0 are the two most commonly used microarray platforms for human samples; the HG-U133 Plus 2.0 platform contains 54 220 probes and the HG-U133A array contains a proper subset (21 722 probes). When different platforms are involved, the subset of common genes is most easily compared. This approach results in the exclusion of substantial measured data and can limit downstream analysis. To predict the expression values for the genes unique to the HG-U133 Plus 2.0 platform, we constructed a series of gene expression inference models based on genes common to both platforms. Our model predicts gene expression values that are within the variability observed in controlled replicate studies and are highly correlated with measured data. Using six previously published studies, we also demonstrate the improved performance of the enlarged feature space generated by our model in downstream analysis.<\/jats:p>\n               <jats:sec>\n                  <jats:title>Availability and Implementation<\/jats:title>\n                  <jats:p>The gene inference model described in this paper is available as a R package (affyImpute), which can be downloaded at http:\/\/simtk.org\/home\/affyimpute.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btw664","type":"journal-article","created":{"date-parts":[[2016,10,18]],"date-time":"2016-10-18T03:05:09Z","timestamp":1476759909000},"page":"522-528","source":"Crossref","is-referenced-by-count":12,"title":["Imputing gene expression to maximize platform compatibility"],"prefix":"10.1093","volume":"33","author":[{"given":"Weizhuang","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Bioengineering, Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lichy","family":"Han","sequence":"additional","affiliation":[{"name":"Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Russ B","family":"Altman","sequence":"additional","affiliation":[{"name":"Department of Bioengineering, Stanford University, Stanford, CA, USA"},{"name":"Department of Genetics, Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2016,11,21]]},"reference":[{"key":"2023020204413617300_btw664-B1","doi-asserted-by":"crossref","DOI":"10.1038\/nature11003","article-title":"The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"2023020204413617300_btw664-B2","doi-asserted-by":"crossref","first-page":"3686","DOI":"10.1158\/1078-0432.CCR-04-2398","article-title":"Patterns of gene expression that characterize long-term survival in advanced stage serous ovarian cancers","volume":"11","author":"Berchuck","year":"2005","journal-title":"Clin. Cancer Res"},{"key":"2023020204413617300_btw664-B3","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1093\/bioinformatics\/19.2.185","article-title":"A comparison of normalization methods for high density oligonucleotide array data based on variance and bias","volume":"19","author":"Bolstad","year":"2003","journal-title":"Bioinformatics"},{"key":"2023020204413617300_btw664-B4","doi-asserted-by":"crossref","first-page":"5478","DOI":"10.1158\/0008-5472.CAN-07-6595","article-title":"A gene signature predicting for survival in suboptimally debulked patients with ovarian cancer","volume":"68","author":"Bonome","year":"2008","journal-title":"Cancer Res"},{"key":"2023020204413617300_btw664-B5","doi-asserted-by":"crossref","first-page":"e175","DOI":"10.1093\/nar\/gni179","article-title":"Evolving gene\/transcript definitions significantly alter the interpretation of GeneChip data","volume":"33","author":"Dai","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"2023020204413617300_btw664-B6","doi-asserted-by":"crossref","first-page":"e1000718","DOI":"10.1371\/journal.pcbi.1000718","article-title":"Using pre-existing microarray datasets to increase experimental power: application to insulin resistance","volume":"6","author":"Daigle","year":"2010","journal-title":"PLoS Comput. Biol"},{"key":"2023020204413617300_btw664-B7","doi-asserted-by":"crossref","first-page":"1846","DOI":"10.1093\/bioinformatics\/btm254","article-title":"GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor","volume":"23","author":"Davis","year":"2007","journal-title":"Bioinformatics"},{"key":"2023020204413617300_btw664-B8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/gb-2008-9-2-r26","article-title":"Correction of technical bias in clinical microarray data improves concordance with known biological information","volume":"9","author":"Eklund","year":"2008","journal-title":"Genome Biol"},{"key":"2023020204413617300_btw664-B9","first-page":"1","article-title":"Regularization Paths for Generalized Linear Models via Coordinate Descent","volume":"1","author":"Friedman","year":"2010","journal-title":"J. Stat. Softw"},{"key":"2023020204413617300_btw664-B10","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1038\/labinvest.2010.66","article-title":"Rhabdoid tumor: gene expression clues to pathogenesis and potential therapeutic targets","volume":"90","author":"Gadd","year":"2010","journal-title":"Lab. Investig. J. Tech. Methods Pathol"},{"key":"2023020204413617300_btw664-B11","first-page":"bat013","article-title":"curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome","volume":"2013","author":"Ganzfried","year":"2013","journal-title":"Database J. Biol. Datab. Curation"},{"key":"2023020204413617300_btw664-B12","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1093\/bioinformatics\/btg405","article-title":"affy\u2014analysis of Affymetrix GeneChip data at the probe level","volume":"20","author":"Gautier","year":"2004","journal-title":"Bioinformatics"},{"key":"2023020204413617300_btw664-B13","doi-asserted-by":"crossref","first-page":"8685","DOI":"10.1073\/pnas.0701361104","article-title":"The human disease network","volume":"104","author":"Goh","year":"2007","journal-title":"Proc. Natl. Acad. Sci. U. S. A"},{"key":"2023020204413617300_btw664-B14","doi-asserted-by":"crossref","first-page":"2978","DOI":"10.1002\/1097-0142(19841215)54:12<2978::AID-CNCR2820541228>3.0.CO;2-Y","article-title":"Clear cell sarcoma of the kidney with emphasis on ultrastructural studies","volume":"54","author":"Haas","year":"1984","journal-title":"Cancer"},{"key":"2023020204413617300_btw664-B15","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/nature12831","article-title":"Inconsistency in large pharmacogenomic studies","volume":"504","author":"Haibe-Kains","year":"2013","journal-title":"Nature"},{"key":"2023020204413617300_btw664-B16","doi-asserted-by":"crossref","first-page":"484-484.","DOI":"10.2307\/2405484","article-title":"The Measurement of Variation","volume":"9","author":"Haldane","year":"1955","journal-title":"Evolution"},{"key":"2023020204413617300_btw664-B17","doi-asserted-by":"crossref","first-page":"532","DOI":"10.3390\/s140100532","article-title":"Sources of high variance between probe signals in affymetrix short oligonucleotide microarrays","volume":"14","author":"Jaksik","year":"2014","journal-title":"Sensors"},{"key":"2023020204413617300_btw664-B18","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/nrc2044","article-title":"The Connectivity Map: a new tool for biomedical research","volume":"7","author":"Lamb","year":"2007","journal-title":"Nat. Rev. Cancer"},{"key":"2023020204413617300_btw664-B19","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1126\/science.1132939","article-title":"The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease","volume":"313","author":"Lamb","year":"2006","journal-title":"Science"},{"key":"2023020204413617300_btw664-B20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-12-474","article-title":"Jetset: selecting the optimal microarray probe set to represent a gene","volume":"12","author":"Li","year":"2011","journal-title":"BMC Bioinformatics"},{"key":"2023020204413617300_btw664-B21","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1093\/bib\/bbq080","article-title":"Missing value imputation for gene expression data: computational techniques to recover missing data from available information","volume":"12","author":"Liew","year":"2011","journal-title":"Brief. Bioinf"},{"key":"2023020204413617300_btw664-B22","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1056\/NEJMe1516564","article-title":"Data sharing","volume":"374","author":"Longo","year":"2016","journal-title":"N. Engl. J. Med"},{"key":"2023020204413617300_btw664-B23","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1038\/nbt1239","article-title":"The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements","volume":"24","author":"MAQC, Consortium","year":"2006","journal-title":"Nat Biotechnol"},{"key":"2023020204413617300_btw664-B24","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1093\/bioinformatics\/btg412","article-title":"APE: Analyses of Phylogenetics and Evolution in R language","volume":"20","author":"Paradis","year":"2004","journal-title":"Bioinformatics"},{"key":"2023020204413617300_btw664-B25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-8-48","article-title":"Improved precision and accuracy for microarrays using updated probe set definitions","volume":"8","author":"Sandberg","year":"2007","journal-title":"BMC Bioinformatics"},{"key":"2023020204413617300_btw664-B26","doi-asserted-by":"crossref","first-page":"5278","DOI":"10.1158\/0008-5472.CAN-05-4610","article-title":"Progression-specific genes identified by expression profiling of matched ductal carcinomas in situ and invasive breast tumors, combining laser capture microdissection and oligonucleotide microarray analysis","volume":"66","author":"Schuetz","year":"2006","journal-title":"Cancer Res"},{"key":"2023020204413617300_btw664-B27","doi-asserted-by":"crossref","first-page":"50","DOI":"10.2307\/2412626","article-title":"Significance tests for coefficients of variation and variability profiles","volume":"29","author":"Sokal","year":"1980","journal-title":"Syst. Zool"},{"key":"2023020204413617300_btw664-B28","doi-asserted-by":"crossref","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"Proc. Natl. Acad. Sci. U. S. A"},{"key":"2023020204413617300_btw664-B29","doi-asserted-by":"crossref","first-page":"4111","DOI":"10.1200\/JCO.2010.28.4273","article-title":"Genomic index of sensitivity to endocrine therapy for breast cancer","volume":"28","author":"Symmans","year":"2010","journal-title":"J. Clin. Oncol"},{"key":"2023020204413617300_btw664-B30","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1093\/bioinformatics\/17.6.520","article-title":"Missing value estimation methods for DNA microarrays","volume":"17","author":"Troyanskaya","year":"2001","journal-title":"Bioinformatics"},{"key":"2023020204413617300_btw664-B31","doi-asserted-by":"crossref","first-page":"S10","DOI":"10.1186\/1471-2105-11-S6-S10","article-title":"Evaluation of gene expression data generated from expired Affymetrix GeneChip(\u00ae) microarrays using MAQC reference RNA samples","volume":"11","author":"Wen","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"2023020204413617300_btw664-B32","doi-asserted-by":"crossref","first-page":"D955","DOI":"10.1093\/nar\/gks1111","article-title":"Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells","volume":"41","author":"Yang","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"2023020204413617300_btw664-B33","doi-asserted-by":"crossref","first-page":"153-153.","DOI":"10.1186\/1471-2164-7-153","article-title":"Identical probes on different high-density oligonucleotide microarrays can produce different measurements of gene expression","volume":"7","author":"Zhang","year":"2006","journal-title":"BMC Genomics"},{"key":"2023020204413617300_btw664-B34","doi-asserted-by":"crossref","first-page":"2798","DOI":"10.1093\/bioinformatics\/btn520","article-title":"GEOmetadb: powerful alternative search engine for the Gene Expression Omnibus","volume":"24","author":"Zhu","year":"2008","journal-title":"Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/33\/4\/522\/49037903\/bioinformatics_33_4_522.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/33\/4\/522\/49037903\/bioinformatics_33_4_522.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T04:46:06Z","timestamp":1675313166000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/33\/4\/522\/2608643"}},"subtitle":[],"editor":[{"given":"Janet","family":"Kelso","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2016,11,21]]},"references-count":34,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2017,2,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btw664","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2017,2,15]]},"published":{"date-parts":[[2016,11,21]]}}}