{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T19:07:17Z","timestamp":1767035237495,"version":"3.37.3"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2017,12,28]],"date-time":"2017-12-28T00:00:00Z","timestamp":1514419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000181","name":"AFOSR","doi-asserted-by":"publisher","award":["YIP FA9550-16-1-0147"],"award-info":[{"award-number":["YIP FA9550-16-1-0147"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,5,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Network alignment (NA) aims to find similar (conserved) regions between networks, such as cellular networks of different species. Until recently, existing methods were limited to aligning static networks. However, real-world systems, including cellular functioning, are dynamic. Hence, in our previous work, we introduced the first ever dynamic NA method, DynaMAGNA++, which improved upon the traditional static NA. However, DynaMAGNA++\u2009does not necessarily scale well to larger networks in terms of alignment quality or runtime.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To address this, we introduce a new dynamic NA approach, DynaWAVE. We show that DynaWAVE complements DynaMAGNA++: while DynaMAGNA++\u2009is more accurate yet slower than DynaWAVE for smaller networks, DynaWAVE is both more accurate and faster than DynaMAGNA++\u2009for larger networks. We provide a friendly user interface and source code for DynaWAVE.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/www.nd.edu\/\u223ccone\/DynaWAVE\/.<\/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\/btx841","type":"journal-article","created":{"date-parts":[[2017,12,27]],"date-time":"2017-12-27T20:18:30Z","timestamp":1514405910000},"page":"1795-1798","source":"Crossref","is-referenced-by-count":17,"title":["Aligning dynamic networks with DynaWAVE"],"prefix":"10.1093","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9435-2159","authenticated-orcid":false,"given":"Vipin","family":"Vijayan","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, ECK Institute for Global Health, and Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA"}]},{"given":"Tijana","family":"Milenkovi\u0107","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, ECK Institute for Global Health, and Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA"}]}],"member":"286","published-online":{"date-parts":[[2017,12,28]]},"reference":[{"key":"2023012810011318300_btx841-B1","doi-asserted-by":"crossref","first-page":"19.","DOI":"10.1186\/s13015-015-0050-8","article-title":"Fair evaluation of global network aligners","volume":"10","author":"Crawford","year":"2015","journal-title":"Algorithms Mol. 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