{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T19:42:20Z","timestamp":1784835740939,"version":"3.55.0"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"Supplement_2","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100010663","name":"European Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010663","name":"ERC","doi-asserted-by":"publisher","award":["770827"],"award-info":[{"award-number":["770827"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Serbian Ministry of Education and Science"},{"DOI":"10.13039\/501100004329","name":"Slovenian Research Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,30]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Molecular interactions have been successfully modeled and analyzed as networks, where nodes represent molecules and edges represent the interactions between them. These networks revealed that molecules with similar local network structure also have similar biological functions. The most sensitive measures of network structure are based on graphlets. However, graphlet-based methods thus far are only applicable to unweighted networks, whereas real-world molecular networks may have weighted edges that can represent the probability of an interaction occurring in the cell. This information is commonly discarded when applying thresholds to generate unweighted networks, which may lead to information loss.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We introduce probabilistic graphlets as a tool for analyzing the local wiring patterns of probabilistic networks. To assess their performance compared to unweighted graphlets, we generate synthetic networks based on different well-known random network models and edge probability distributions and demonstrate that probabilistic graphlets outperform their unweighted counterparts in distinguishing network structures. Then we model different real-world molecular interaction networks as weighted graphs with probabilities as weights on edges and we analyze them with our new weighted graphlets-based methods. We show that due to their probabilistic nature, probabilistic graphlet-based methods more robustly capture biological information in these data, while simultaneously showing a higher sensitivity to identify condition-specific functions compared to their unweighted graphlet-based method counterparts.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availabilityand implementation<\/jats:title><jats:p>Our implementation of probabilistic graphlets is available at https:\/\/github.com\/Serdobe\/Probabilistic_Graphlets.<\/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\/btaa812","type":"journal-article","created":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T11:11:23Z","timestamp":1599563483000},"page":"i804-i812","source":"Crossref","is-referenced-by-count":8,"title":["Probabilistic graphlets capture biological function in probabilistic molecular networks"],"prefix":"10.1093","volume":"36","author":[{"given":"Sergio","family":"Doria-Belenguer","sequence":"first","affiliation":[{"name":"Barcelona Supercomputing Center , Barcelona 08034, Spain"},{"name":"Universitat Polit\u00e8cnica de Catalunya (UPC) , Barcelona 08034, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus K.","family":"Youssef","sequence":"additional","affiliation":[{"name":"Barcelona Supercomputing Center , Barcelona 08034, Spain"},{"name":"Universitat Polit\u00e8cnica de Catalunya (UPC) , Barcelona 08034, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ren\u00e9","family":"B\u00f6ttcher","sequence":"additional","affiliation":[{"name":"Barcelona Supercomputing Center , Barcelona 08034, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"No\u00ebl","family":"Malod-Dognin","sequence":"additional","affiliation":[{"name":"Barcelona Supercomputing Center , Barcelona 08034, Spain"},{"name":"Department of Computer Science, University College London , London WC1E 6BT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nata\u0161a","family":"Pr\u017eulj","sequence":"additional","affiliation":[{"name":"Barcelona Supercomputing Center , Barcelona 08034, Spain"},{"name":"Department of Computer Science, University College London , London WC1E 6BT, UK"},{"name":"ICREA , Barcelona 08010, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,12,29]]},"reference":[{"key":"2023062409322753200_btaa812-B1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene Ontology: tool for the unification of biology","volume":"25","author":"Ashburner","year":"2000","journal-title":"Nat. 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