{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T13:17:54Z","timestamp":1786713474733,"version":"3.56.0"},"reference-count":13,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2009,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Researchers in the field of bioinformatics often face a challenge of combining several ordered lists in a proper and efficient manner. Rank aggregation techniques offer a general and flexible framework that allows one to objectively perform the necessary aggregation. With the rapid growth of high-throughput genomic and proteomic studies, the potential utility of rank aggregation in the context of meta-analysis becomes even more apparent. One of the major strengths of rank-based aggregation is the ability to combine lists coming from different sources and platforms, for example different microarray chips, which may or may not be directly comparable otherwise.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The <jats:italic>RankAggreg<\/jats:italic> package provides two methods for combining the ordered lists: the Cross-Entropy method and the Genetic Algorithm. Two examples of rank aggregation using the package are given in the manuscript: one in the context of clustering based on gene expression, and the other one in the context of meta-analysis of prostate cancer microarray experiments.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>The two examples described in the manuscript clearly show the utility of the <jats:italic>RankAggreg<\/jats:italic> package in the current bioinformatics context where ordered lists are routinely produced as a result of modern high-throughput technologies.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-10-62","type":"journal-article","created":{"date-parts":[[2009,2,19]],"date-time":"2009-02-19T19:17:02Z","timestamp":1235071022000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":271,"title":["RankAggreg, an R package for weighted rank aggregation"],"prefix":"10.1186","volume":"10","author":[{"given":"Vasyl","family":"Pihur","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susmita","family":"Datta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Somnath","family":"Datta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2009,2,19]]},"reference":[{"key":"2792_CR1","doi-asserted-by":"crossref","first-page":"Article 15","DOI":"10.2202\/1544-6115.1204","volume":"5","author":"R DeConde","year":"2006","unstructured":"DeConde R, Hawley S, Falcon S, Clegg N, Knudsen B, Etzioni R: Combining results of microarray experiments: a rank aggregation approach. 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