{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T16:40:01Z","timestamp":1761237601666},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"S1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2009,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Considerable efforts have been made to extract protein-protein interactions from the biological literature, but little work has been done on the extraction of interaction detection methods. It is crucial to annotate the detection methods in the literature, since different detection methods shed different degrees of reliability on the reported interactions. However, the diversity of method mentions in the literature makes the automatic extraction quite challenging.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>In this article, we develop a generative topic model, the Correlated Method-Word model (<jats:italic>CMW<\/jats:italic> model) to extract the detection methods from the literature. In the <jats:italic>CMW<\/jats:italic> model, we formulate the correlation between the different methods and related words in a probabilistic framework in order to infer the potential methods from the given document. By applying the model on a corpus of 5319 full text documents annotated by the <jats:italic>MINT<\/jats:italic> and <jats:italic>IntAct<\/jats:italic> databases, we observe promising results, which outperform the best result reported in the <jats:italic>BioCreative II<\/jats:italic> challenge evaluation.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>From the promising experiment results, we can see that the <jats:italic>CMW<\/jats:italic> model overcomes the issues caused by the diversity in the method mentions and properly captures the in-depth correlations between the detection methods and related words. The performance outperforming the baseline methods confirms that the dependence assumptions of the model are reasonable and the model is competent for the practical processing.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-10-s1-s55","type":"journal-article","created":{"date-parts":[[2009,1,30]],"date-time":"2009-01-30T20:05:15Z","timestamp":1233345915000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Extract interaction detection methods from the biological literature"],"prefix":"10.1186","volume":"10","author":[{"given":"Hongning","family":"Wang","sequence":"first","affiliation":[]},{"given":"Minlie","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Xiaoyan","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2009,1,30]]},"reference":[{"issue":"5","key":"3238_CR1","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1093\/bioinformatics\/btg452","volume":"20","author":"N Daraselia","year":"2004","unstructured":"Daraselia N, Yuryev A, Egorov S, Novichkova S, Nikitin A, Mazo I: Extracting human protein interactions from MEDLINE using a full-sentence parser. 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