{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:01Z","timestamp":1772138041660,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/R512333\/1"],"award-info":[{"award-number":["EP\/R512333\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/L016044\/1"],"award-info":[{"award-number":["EP\/L016044\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000268","name":"Biotechnology and Biological Sciences Research Council","doi-asserted-by":"publisher","award":["BB\/T001801\/1"],"award-info":[{"award-number":["BB\/T001801\/1"]}],"id":[{"id":"10.13039\/501100000268","id-type":"DOI","asserted-by":"publisher"}]},{"name":"COSTNET COST Action","award":["CA15109"],"award-info":[{"award-number":["CA15109"]}]},{"name":"Oxford-Emirates Data Science Lab"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Even within well-studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes\/proteins, using a network of gene coexpression data that includes functional annotations. However, the lack of trustworthy functional annotations can impede the validation of such networks. Hence, there is a need for a principled method to construct gene coexpression networks that capture biological information and are structurally stable even in the absence of functional information.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We introduce the concept of signed distance correlation as a measure of dependency between two variables, and apply it to generate gene coexpression networks. Distance correlation offers a more intuitive approach to network construction than commonly used methods, such as Pearson correlation and mutual information. We propose a framework to generate self-consistent networks using signed distance correlation purely from gene expression data, with no additional information. We analyse data from three different organisms to illustrate how networks generated with our method are more stable and capture more biological information compared to networks obtained from Pearson correlation or mutual information.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Code is available online (https:\/\/github.com\/javier-pardodiaz\/sdcorGCN).<\/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\/btab041","type":"journal-article","created":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T08:37:32Z","timestamp":1611131852000},"page":"1982-1989","source":"Crossref","is-referenced-by-count":11,"title":["Robust gene coexpression networks using signed distance correlation"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9262-0482","authenticated-orcid":false,"given":"Javier","family":"Pardo-Diaz","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Oxford , Oxford OX1 3LB, UK"},{"name":"Department of Plant Sciences, University of Oxford , Oxford OX1 3RB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2784-2040","authenticated-orcid":false,"given":"Lyuba V","family":"Bozhilova","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Oxford , Oxford OX1 3LB, UK"}]},{"given":"Mariano","family":"Beguerisse-D\u00edaz","sequence":"additional","affiliation":[{"name":"Mathematical Institute, University of Oxford , Oxford OX2 6GG, UK"}]},{"given":"Philip S","family":"Poole","sequence":"additional","affiliation":[{"name":"Department of Plant Sciences, University of Oxford , Oxford OX1 3RB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1388-2252","authenticated-orcid":false,"given":"Charlotte M","family":"Deane","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Oxford , Oxford OX1 3LB, UK"}]},{"given":"Gesine","family":"Reinert","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Oxford , Oxford OX1 3LB, UK"}]}],"member":"286","published-online":{"date-parts":[[2021,2,1]]},"reference":[{"key":"2023061310301865300_btab041-B1","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1038\/nbt890","article-title":"Computational discovery of gene modules and regulatory networks","volume":"21","author":"Bar-Joseph","year":"2003","journal-title":"Nat. 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