Toward an automatic method for extracting cancer- and other disease-related point mutations from the biomedical literature.

ABSTRACT:

Motivation

A major goal of biomedical research in personalized medicine is to find relationships between mutations and their corresponding disease phenotypes. However, most of the disease-related mutational data are currently buried in the biomedical literature in textual form and lack the necessary structure to allow easy retrieval and visualization. We introduce a high-throughput computational method for the identification of relevant disease mutations in PubMed abstracts applied to prostate (PCa) and breast cancer (BCa) mutations.

Results

We developed the extractor of mutations (EMU) tool to identify mutations and their associated genes. We benchmarked EMU against MutationFinder--a tool to extract point mutations from text. Our results show that both methods achieve compa

SUBMITTER: Doughty E 

PROVIDER: S-EPMC3031038 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature