{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:13:08Z","timestamp":1760710388891},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T00:00:00Z","timestamp":1606176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6,16]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Alignment of protein\u2013protein interaction networks can be used for the unsupervised prediction of functional modules, such as protein complexes and signaling pathways, that are conserved across different species. To date, various algorithms have been proposed for biological network alignment, many of which attempt to incorporate topological similarity between the networks into the alignment process with the goal of constructing accurate and biologically meaningful alignments. Especially, random walk models have been shown to be effective for quantifying the global topological relatedness between nodes that belong to different networks by diffusing node-level similarity along the interaction edges. However, these schemes are not ideal for capturing the local topological similarity between nodes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this article, we propose MONACO, a novel and versatile network alignment algorithm that finds highly accurate pairwise and multiple network alignments through the iterative optimal matching of \u2018local\u2019 neighborhoods around focal nodes. Extensive performance assessment based on real networks as well as synthetic networks, for which the ground truth is known, demonstrates that MONACO clearly and consistently outperforms all other state-of-the-art network alignment algorithms that we have tested, in terms of accuracy, coherence and topological quality of the aligned network regions. Furthermore, despite the sharply enhanced alignment accuracy, MONACO remains computationally efficient and it scales well with increasing size and number of networks.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>Matlab implementation is freely available at https:\/\/github.com\/bjyoontamu\/MONACO.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa962","type":"journal-article","created":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T20:13:55Z","timestamp":1604348035000},"page":"1401-1410","source":"Crossref","is-referenced-by-count":9,"title":["MONACO: accurate biological network alignment through optimal neighborhood matching between focal nodes"],"prefix":"10.1093","volume":"37","author":[{"given":"Hyun-Myung","family":"Woo","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas A&M University , College Station, TX 77843, USA"}]},{"given":"Byung-Jun","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas A&M University , College Station, TX 77843, USA"},{"name":"TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University , College Station, TX 77845, USA"},{"name":"Computational Science Initiative, Brookhaven National Laboratory , Upton, NY 11973, USA"}]}],"member":"286","published-online":{"date-parts":[[2020,11,24]]},"reference":[{"key":"2023051709334830500_btaa962-B1","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1093\/bioinformatics\/btt071","article-title":"SPINAL: scalable protein interaction network alignment","volume":"29","author":"Alada\u011f","year":"2013","journal-title":"Bioinformatics"},{"key":"2023051709334830500_btaa962-B2","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1093\/bioinformatics\/btt713","article-title":"BEAMS: backbone extraction and merge strategy for the global many-to-many alignment of multiple PPI networks","volume":"30","author":"Alkan","year":"2014","journal-title":"Bioinformatics"},{"first-page":"475","year":"2006","author":"Andersen","key":"2023051709334830500_btaa962-B3"},{"key":"2023051709334830500_btaa962-B4","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1101\/gr.4526006","article-title":"Systematic identification of functional orthologs based on protein network comparison","volume":"16","author":"Bandyopadhyay","year":"2006","journal-title":"Genome Res"},{"key":"2023051709334830500_btaa962-B5","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1093\/bioinformatics\/btv063","article-title":"A multiobjective memetic algorithm for PPI network alignment","volume":"31","author":"Clark","year":"2015","journal-title":"Bioinformatics"},{"key":"2023051709334830500_btaa962-B6","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1074\/mcp.M600381-MCP200","article-title":"Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae","volume":"6","author":"Collins","year":"2007","journal-title":"Mol. 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