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dc.contributor.authorPapamokos, Georgeen_US
dc.contributor.authorSilins, Ilonaen_US
dc.date.accessioned2016-11-18T20:07:22Z
dc.date.issued2016en_US
dc.identifier.citationPapamokos, George, and Ilona Silins. 2016. “Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.” Frontiers in Pharmacology 7 (1): 284. doi:10.3389/fphar.2016.00284. http://dx.doi.org/10.3389/fphar.2016.00284.en
dc.identifier.issn1663-9812en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:29407831
dc.description.abstractThere is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.en
dc.language.isoen_USen
dc.publisherFrontiers Media S.A.en
dc.relation.isversionofdoi:10.3389/fphar.2016.00284en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC5003827/pdf/en
dash.licenseLAAen_US
dc.subjectMethodsen
dc.subjectcarcinogensen
dc.subjectmode of actionen
dc.subjecttext miningen
dc.subjectQSARen
dc.subjectrisk assessmenten
dc.subjecttoxicityen
dc.subjectpredictionen
dc.titleCombining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Actionen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalFrontiers in Pharmacologyen
dc.date.available2016-11-18T20:07:22Z
dc.identifier.doi10.3389/fphar.2016.00284*


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