{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T13:20:26Z","timestamp":1777555226452,"version":"3.51.4"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2016,10,12]],"date-time":"2016-10-12T00:00:00Z","timestamp":1476230400000},"content-version":"vor","delay-in-days":348,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Nowadays, knowledge extraction methods from Next Generation Sequencing data are highly requested. In this work, we focus on RNA-seq gene expression analysis and specifically on case\u2013control studies with rule-based supervised classification algorithms that build a model able to discriminate cases from controls. State of the art algorithms compute a single classification model that contains few features (genes). On the contrary, our goal is to elicit a higher amount of knowledge by computing many classification models, and therefore to identify most of the genes related to the predicted class.<\/jats:p><jats:p>Results: We propose CAMUR, a new method that extracts multiple and equivalent classification models. CAMUR iteratively computes a rule-based classification model, calculates the power set of the genes present in the rules, iteratively eliminates those combinations from the data set, and performs again the classification procedure until a stopping criterion is verified. CAMUR includes an ad-hoc knowledge repository (database) and a querying tool.<\/jats:p><jats:p>We analyze three different types of RNA-seq data sets (Breast, Head and Neck, and Stomach Cancer) from The Cancer Genome Atlas (TCGA) and we validate CAMUR and its models also on non-TCGA data. Our experimental results show the efficacy of CAMUR: we obtain several reliable equivalent classification models, from which the most frequent genes, their relationships, and the relation with a particular cancer are deduced.<\/jats:p><jats:p>Availability and implementation: \u00a0dmb.iasi.cnr.it\/camur.php<\/jats:p><jats:p>Contact: \u00a0emanuel@iasi.cnr.it<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv635","type":"journal-article","created":{"date-parts":[[2015,10,31]],"date-time":"2015-10-31T02:38:11Z","timestamp":1446259091000},"page":"697-704","source":"Crossref","is-referenced-by-count":33,"title":["CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules"],"prefix":"10.1093","volume":"32","author":[{"given":"Valerio","family":"Cestarelli","sequence":"first","affiliation":[{"name":"1 Institute of Systems Analysis and Computer Science \u2013 National Research Council, 00185, Rome, Italy,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giulia","family":"Fiscon","sequence":"additional","affiliation":[{"name":"1 Institute of Systems Analysis and Computer Science \u2013 National Research Council, 00185, Rome, Italy,"},{"name":"2 Department of Computer, Control, and Management Engineering \u2013 Sapienza University, 00185, Rome, Italy and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giovanni","family":"Felici","sequence":"additional","affiliation":[{"name":"1 Institute of Systems Analysis and Computer Science \u2013 National Research Council, 00185, Rome, Italy,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paola","family":"Bertolazzi","sequence":"additional","affiliation":[{"name":"1 Institute of Systems Analysis and Computer Science \u2013 National Research Council, 00185, Rome, Italy,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emanuel","family":"Weitschek","sequence":"additional","affiliation":[{"name":"1 Institute of Systems Analysis and Computer Science \u2013 National Research Council, 00185, Rome, Italy,"},{"name":"3 Department of Engineering \u2013 Uninettuno International University, Corso Vittorio Emanuele II, 39 \u2013 00186 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2015,10,30]]},"reference":[{"key":"2023020110432868300_btv635-B1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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