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dc.contributor.authorPospisil, Pavel
dc.contributor.authorIyer, Lakshmanan K
dc.contributor.authorAdelstein, Stanley
dc.contributor.authorKassis, Amin
dc.date.accessioned2018-12-18T15:59:50Z
dc.date.issued2006
dc.identifier.citationPospisil, P., L. K. Iyer, S. J. Adelstein, and A. I. Kassis. 2006. A Combined Approach to Data Mining of Textual and Structured Data to Identify Cancer-Related Targets. BMC Bioinformatics 7, no. 354.en_US
dc.identifier.issn1471-2105en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:37933098*
dc.description.abstractBackground: We present an effective, rapid, systematic data mining approach for identifying genes or proteins related to a particular interest. A selected combination of programs exploring PubMed abstracts, universal gene/protein knowledge bases (LSGraph and Ingenuity Pathway Analysis) was assembled to distinguish enzymes with hydrolytic activities that are expressed in the extracellular space of cancer cells. Proteins were identified with respect to six types of cancer occurring in the prostate, breast, lung, colon, ovary, and pancreas.Results: The data mining method identified previously undetected targets. Our combined strategy applied to each cancer type identified a minimum of 375 proteins expressed within the extracellular space and/or attached to the plasma membrane. The method led to the recognition of human cancer-related hydrolases (on average, similar to 35 per cancer type), among which were prostatic acid phosphatase, prostate-specific antigen, and sulfatase 1.Conclusion: The combined data mining of several databases overcame many of the limitations of querying a single database and enabled the facile identification of gene products. In the case of cancer-related targets, it produced a list of putative extracellular, hydrolytic enzymes that merit additional study as candidates for cancer radioimaging and radiotherapy. The proposed data mining strategy is of a general nature and can be applied to other biological databases for understanding biological functions and diseases.en_US
dc.language.isoen_USen_US
dc.publisherSpringer Natureen_US
dash.licenseLAA
dc.titleA Combined Approach to Data Mining of Textual and Structured Data to Identify Cancer-Related Targetsen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalBMC Bioinformaticsen_US
dash.depositing.authorAdelstein, Stanley
dc.date.available2018-12-18T15:59:50Z
dash.workflow.comments1Science Serial ID 17125en_US
dc.identifier.doi10.1186/1471-2105-7-354
dc.source.journalBMC Bioinformatics
dash.source.volume7;1
dash.source.page354
dash.contributor.affiliatedKassis, Amin
dash.contributor.affiliatedAdelstein, Stanley


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