{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T00:38:16Z","timestamp":1775176696371,"version":"3.50.1"},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2017,12,11]],"date-time":"2017-12-11T00:00:00Z","timestamp":1512950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,4,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>RNA-Seq is becoming the technique of choice for high-throughput transcriptome profiling, which, besides class comparison for differential expression, promises to be an effective and powerful tool for biomarker discovery. However, a systematic analysis of high-dimensional genomic data is a demanding task for such a purpose. DaMiRseq offers an organized, flexible and convenient framework to remove noise and bias, select the most informative features and perform accurate classification.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>DaMiRseq is developed for the R environment (R \u2265\u20093.4) and is released under GPL (\u22652) License. The package runs on Windows, Linux and Macintosh operating systems and is freely available to non-commercial users at the Bioconductor open-source, open-development software project repository (https:\/\/bioconductor.org\/packages\/DaMiRseq\/). In compliance with Bioconductor standards, the authors ensure stable package maintenance through software and documentation updates.<\/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\/btx795","type":"journal-article","created":{"date-parts":[[2017,12,6]],"date-time":"2017-12-06T12:10:16Z","timestamp":1512562216000},"page":"1416-1418","source":"Crossref","is-referenced-by-count":69,"title":["DaMiRseq\u2014an R\/Bioconductor package for data mining of RNA-Seq data: normalization, feature selection and classification"],"prefix":"10.1093","volume":"34","author":[{"given":"Mattia","family":"Chiesa","sequence":"first","affiliation":[{"name":"Immunology and Functional Genomics Unit, Centro Cardiologico Monzino, IRCCS, Milan, Italy"}]},{"given":"Gualtiero I","family":"Colombo","sequence":"additional","affiliation":[{"name":"Immunology and Functional Genomics Unit, Centro Cardiologico Monzino, IRCCS, Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1022-4481","authenticated-orcid":false,"given":"Luca","family":"Piacentini","sequence":"additional","affiliation":[{"name":"Immunology and Functional Genomics Unit, Centro Cardiologico Monzino, IRCCS, Milan, Italy"}]}],"member":"286","published-online":{"date-parts":[[2017,12,11]]},"reference":[{"key":"2023012713005779200_btx795-B1","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/0169-7439(87)80067-9","article-title":"Intermediate least squares regression method","volume":"1","author":"Ildiko","year":"1987","journal-title":"Chemometr. 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