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The existing paradigm for studying protein sequence, structure, function and evolution has been established based on the assumption that the protein universe is discrete and hierarchical. Cumulative evidence suggests that the protein universe is continuous. As a result, conventional sequence homology search methods may be not able to detect novel structural, functional and evolutionary relationships between proteins from weak and noisy sequence signals. To overcome the limitations in existing similarity search methods, we propose a new algorithmic framework\u2014Enrichment of Network Topological Similarity (ENTS)\u2014to improve the performance of large scale similarity searches in bioinformatics.<\/jats:p><jats:p>Results: We apply ENTS to a challenging unsolved problem: protein fold recognition. Our rigorous benchmark studies demonstrate that ENTS considerably outperforms state-of-the-art methods. As the concept of ENTS can be applied to any similarity metric, it may provide a general framework for similarity search on any set of biological entities, given their representation as a network.<\/jats:p><jats:p>Availability and implementation: Source code freely available upon request<\/jats:p><jats:p>Contact: lxie@iscb.org<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv125","type":"journal-article","created":{"date-parts":[[2015,2,26]],"date-time":"2015-02-26T15:34:19Z","timestamp":1424964859000},"page":"2106-2114","source":"Crossref","is-referenced-by-count":12,"title":["A new method to improve network topological similarity search: applied to fold recognition"],"prefix":"10.1093","volume":"31","author":[{"given":"John","family":"Lhota","sequence":"first","affiliation":[{"name":"1 Hunter College High School, New York, NY 10128, U.S.A., 2Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, U.S.A., 3Department of Biological Sciences, Hunter College, The City University of New York New York, NY 10065, U.S.A. and 4The Graduate Center, The City University of New York, New York, NY 10016, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruth","family":"Hauptman","sequence":"additional","affiliation":[{"name":"1 Hunter College High School, New York, NY 10128, U.S.A., 2Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, U.S.A., 3Department of Biological Sciences, Hunter College, The City University of New York New York, NY 10065, U.S.A. and 4The Graduate Center, The City University of New York, New York, NY 10016, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Hart","sequence":"additional","affiliation":[{"name":"1 Hunter College High School, New York, NY 10128, U.S.A., 2Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, U.S.A., 3Department of Biological Sciences, Hunter College, The City University of New York New York, NY 10065, U.S.A. and 4The Graduate Center, The City University of New York, New York, NY 10016, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Clara","family":"Ng","sequence":"additional","affiliation":[{"name":"1 Hunter College High School, New York, NY 10128, U.S.A., 2Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, U.S.A., 3Department of Biological Sciences, Hunter College, The City University of New York New York, NY 10065, U.S.A. and 4The Graduate Center, The City University of New York, New York, NY 10016, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Xie","sequence":"additional","affiliation":[{"name":"1 Hunter College High School, New York, NY 10128, U.S.A., 2Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, U.S.A., 3Department of Biological 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