{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T20:47:41Z","timestamp":1648932461367},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2011,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Regulation of cellular events is, often, initiated via extracellular signaling. Extracellular signaling occurs when a circulating ligand interacts with one or more membrane-bound receptors. Identification of receptor-ligand pairs is thus an important and specific form of PPI prediction.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Given a set of disparate data sources (expression data, domain content, and phylogenetic profile) we seek to predict new receptor-ligand pairs. We create a combined kernel classifier and assess its performance with respect to the Database of Ligand-Receptor Partners (DLRP) 'golden standard' as well as the method proposed by Gertz <jats:italic>et al.<\/jats:italic> Among our findings, we discover that our predictions for the tgf\u03b2 family accurately reconstruct over 76% of the supported edges (0.76 recall and 0.67 precision) of the receptor-ligand bipartite graph defined by the DLRP \"golden standard\". In addition, for the tgf\u03b2 family, the combined kernel classifier is able to relatively improve upon the Gertz <jats:italic>et al.<\/jats:italic> work by a factor of approximately 1.5 when considering that our method has an <jats:italic>F<\/jats:italic>-measure of 0.71 while that of Gertz <jats:italic>et al.<\/jats:italic> has a value of 0.48.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>The prediction of receptor-ligand pairings is a difficult and complex task. We have demonstrated that using kernel learning on multiple data sources provides a stronger alternative to the existing method in solving this task.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-12-336","type":"journal-article","created":{"date-parts":[[2011,8,12]],"date-time":"2011-08-12T06:15:47Z","timestamp":1313129747000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Predicting receptor-ligand pairs through kernel learning"],"prefix":"10.1186","volume":"12","author":[{"given":"Ernesto","family":"Iacucci","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabian","family":"Ojeda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bart","family":"De Moor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yves","family":"Moreau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2011,8,11]]},"reference":[{"key":"4739_CR1","doi-asserted-by":"publisher","first-page":"W315","DOI":"10.1093\/nar\/gkl112","volume":"34","author":"JM Izarzugaza","year":"2006","unstructured":"Izarzugaza JM, Juan D, Pons C, Ranea JA, Valencia A, Pazos F: TSEMA: interactive prediction of protein pairings between interacting families. 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