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A key component for such studies is the alignment of networks.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We introduce the Bioconductor package <jats:italic>GraphAlignment<\/jats:italic> for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, <jats:italic>Gr\u00e6mlin 2.0<\/jats:italic>.<\/jats:p>\n            <jats:p>On simulated data, <jats:italic>GraphAlignment<\/jats:italic> outperforms <jats:italic>Gr\u00e6mlin 2.0<\/jats:italic> in several benchmarks except for computational complexity. When there is little or no noise in the data, <jats:italic>GraphAlignment<\/jats:italic> is slower than <jats:italic>Gr\u00e6mlin 2.0<\/jats:italic>. It is faster than <jats:italic>Gr\u00e6mlin 2.0<\/jats:italic> when processing noisy data containing spurious vertex associations. Its typical case complexity grows approximately as<jats:inline-formula>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>O<\/mml:mi>\n                  <mml:mo>(<\/mml:mo>\n                  <mml:msup>\n                    <mml:mrow>\n                      <mml:mi>N<\/mml:mi>\n                    <\/mml:mrow>\n                    <mml:mrow>\n                      <mml:mn>2<\/mml:mn>\n                      <mml:mi>.<\/mml:mi>\n                      <mml:mn>6<\/mml:mn>\n                    <\/mml:mrow>\n                  <\/mml:msup>\n                  <mml:mo>)<\/mml:mo>\n                <\/mml:math>\n              <\/jats:inline-formula>.<\/jats:p>\n            <jats:p>On empirical bacterial protein-protein interaction networks (PIN) and gene co-expression networks, <jats:italic>GraphAlignment<\/jats:italic> outperforms <jats:italic>Gr\u00e6mlin 2.0<\/jats:italic> with respect to coverage and specificity, albeit by a small margin. On large eukaryotic PIN, <jats:italic>Gr\u00e6mlin 2.0<\/jats:italic> outperforms <jats:italic>GraphAlignment<\/jats:italic>.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>The <jats:italic>GraphAlignment<\/jats:italic> algorithm is robust to spurious vertex associations, correctly resolves paralogs, and shows very good performance in identification of homologous vertices defined by high vertex and\/or interaction similarity. The simplicity and generality of <jats:italic>GraphAlignment<\/jats:italic> edge scoring makes the algorithm an appropriate choice for global alignment of networks.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1752-0509-6-144","type":"journal-article","created":{"date-parts":[[2012,11,21]],"date-time":"2012-11-21T13:14:36Z","timestamp":1353503676000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["GraphAlignment: Bayesian pairwise alignment of biological networks"],"prefix":"10.1186","volume":"6","author":[{"given":"Michal","family":"Kol\u00e1\u0159","sequence":"first","affiliation":[]},{"given":"J\u00f6rn","family":"Meier","sequence":"additional","affiliation":[]},{"given":"Ville","family":"Mustonen","sequence":"additional","affiliation":[]},{"given":"Michael","family":"L\u00e4ssig","sequence":"additional","affiliation":[]},{"given":"Johannes","family":"Berg","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2012,11,21]]},"reference":[{"key":"1045_CR1","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1093\/nar\/27.1.29","volume":"27","author":"H Ogata","year":"1999","unstructured":"Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M: KEGG: Kyoto Encyclopedia of Genes and Genomes. 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