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Traditional NA assumes that it is topological similarity (isomorphic-like matching) between network regions that corresponds to the regions\u2019 functional relatedness. However, we recently found that functionally unrelated proteins are as topologically similar as functionally related proteins. So, we redefined NA as a data-driven method called TARA, which learns from network and protein functional data what kind of topological<jats:italic>relatedness<\/jats:italic>(rather than similarity) between proteins corresponds to their functional relatedness. TARA used topological information (within each network) but not sequence information (between proteins across networks). Yet, TARA yielded higher protein functional prediction accuracy than existing NA methods, even those that used both topological and sequence information.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Here, we propose TARA++ that is also data-driven, like TARA and unlike other existing methods, but that uses across-network sequence information on top of within-network topological information, unlike TARA. To deal with the within-and-across-network analysis, we adapt social network embedding to the problem of biological NA. TARA++ outperforms protein functional prediction accuracy of existing methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>As such, combining research knowledge from different domains is promising. Overall, improvements in protein functional prediction have biomedical implications, for example allowing researchers to better understand how cancer progresses or how humans age.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-021-03971-6","type":"journal-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T16:03:10Z","timestamp":1611936190000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Data-driven biological network alignment that uses topological, sequence, and functional information"],"prefix":"10.1186","volume":"22","author":[{"given":"Shawn","family":"Gu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8023-6907","authenticated-orcid":false,"given":"Tijana","family":"Milenkovi\u0107","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,29]]},"reference":[{"issue":"20","key":"3971_CR1","doi-asserted-by":"publisher","first-page":"11495","DOI":"10.1093\/nar\/gkx937","volume":"45","author":"KW Ellens","year":"2017","unstructured":"Ellens KW, Christian N, Singh C, Satagopam VP, May P, Linster CL. 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