A combined approach to data mining of textual and structured data to identify cancer-related targets |
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| Authors: | Pavel Pospisil Lakshmanan K Iyer S James Adelstein and Amin I Kassis |
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| Institution: | (1) Department of Radiology, Harvard Medical School, 200 Longwood Avenue, Boston, Massachusetts, USA;(2) Bauer Center for Genomics Research, Harvard University, 7 Divinity Avenue, Cambridge, Massachusetts, USA |
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| Abstract: | Background 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 databases (UniProt, InterPro,
NCBI Entrez), and state-of-the-art pathway 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. |
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