A combined approach to data mining of textual and structured data to identify cancer-related targets.

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.

Results

The data mining method identified previously undetected targets. Our combined strategy applied to each cancer type identified a minimum of 375 proteins expressed

SUBMITTER: Pospisil P 

PROVIDER: S-EPMC1555615 | biostudies-literature | 2006 Jul

REPOSITORIES: biostudies-literature