{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T17:52:06Z","timestamp":1763747526986},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2016,11,10]],"date-time":"2016-11-10T00:00:00Z","timestamp":1478736000000},"content-version":"vor","delay-in-days":73,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>It is often the case in biological measurement data that results are given as a ranked list of quantities\u2014for example, differential expression (DE) of genes as inferred from microarrays or RNA-seq. Recent years brought considerable progress in statistical tools for enrichment analysis in ranked lists. Several tools are now available that allow users to break the fixed set paradigm in assessing statistical enrichment of sets of genes. Continuing with the example, these tools identify factors that may be associated with measured differential expression. A drawback of existing tools is their focus on identifying single factors associated with the observed or measured ranks, failing to address relationships between these factors. For example, a scenario in which genes targeted by multiple miRNAs play a central role in the DE signal but the effect of each single miRNA is too subtle to be detected, as shown in our results.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose statistical and algorithmic approaches for selecting a sub-collection of factors that can be aggregated into one ranked list that is heuristically most associated with an input ranked list (pivot). We examine performance on simulated data and apply our approach to cancer datasets. We find small sub-collections of miRNA that are statistically associated with gene DE in several types of cancer, suggesting miRNA cooperativity in driving disease related processes. Many of our findings are consistent with known roles of miRNAs in cancer, while others suggest previously unknown roles for certain miRNAs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and Implementation<\/jats:title>\n                  <jats:p>Code and instructions for our algorithmic framework, MULSEA, are in: https:\/\/github.com\/YakhiniGroup\/MULSEA.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Contact<\/jats:title>\n                  <jats:p>dalia.cohn@gmail.com<\/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\/btw435","type":"journal-article","created":{"date-parts":[[2016,9,1]],"date-time":"2016-09-01T07:53:39Z","timestamp":1472716419000},"page":"i464-i472","source":"Crossref","is-referenced-by-count":6,"title":["Mutual enrichment in aggregated ranked lists with applications to gene expression regulation"],"prefix":"10.1093","volume":"32","author":[{"given":"Dalia","family":"Cohn-Alperovich","sequence":"first","affiliation":[{"name":"Computer Science Department, Technion \u2013 Israel Institute of Technology, Haifa 3200003, Israel,"},{"name":"Microsoft Research and Development Center, Haifa and Herzeliya, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alona","family":"Rabner","sequence":"additional","affiliation":[{"name":"Department of Biology, Technion \u2013 Israel Institute of Technology, Haifa 3200003, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ilona","family":"Kifer","sequence":"additional","affiliation":[{"name":"Microsoft Research and Development Center, Haifa and Herzeliya, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yael","family":"Mandel-Gutfreund","sequence":"additional","affiliation":[{"name":"Department of Biology, Technion \u2013 Israel Institute of Technology, Haifa 3200003, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zohar","family":"Yakhini","sequence":"additional","affiliation":[{"name":"Computer Science Department, Technion \u2013 Israel Institute of Technology, Haifa 3200003, Israel,"},{"name":"School of Computer Science, The Interdisciplinary Center, Herzeliya 4610101, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2016,8,29]]},"reference":[{"key":"2023020113251385000_btw435-B1","doi-asserted-by":"crossref","first-page":"e05005","DOI":"10.7554\/eLife.05005","article-title":"Predicting effective microRNA target sites in mammalian mRNAs","volume":"4","author":"Agarwal","year":"2015","journal-title":"Elife"},{"key":"2023020113251385000_btw435-B2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: tool for the unification of biology. 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